Datasets:

cy0307
/

awesome-egocentric-atlas

@@ -27,17 +27,11 @@ Reference docs:
 - [docs/status_policy.md](docs/status_policy.md) — how each `status` is decided.
 - [docs/taxonomy.md](docs/taxonomy.md) — task/modality vocabulary; add new tokens to [`data/taxonomy.yml`](data/taxonomy.yml).
-## Validate Before You Open a PR
-The catalog is machine-checked. Run the validator locally (Ruby 2.6+, no gems needed):
-```bash
-ruby scripts/validate_catalog.rb
-ruby scripts/build_artifacts.rb --check
-ruby scripts/build_hf_package.rb --check
-```
-These checks validate the catalog shape, allowed `kind` and `status` values, README local links/assets, README/catalog table coverage, generated CSV/JSON/SVG/README snippets, and the Hugging Face upload package. The same checks run automatically in CI on every push and pull request, so a green check means your entry is structurally consistent.
 ## Inclusion Policy

 - [docs/status_policy.md](docs/status_policy.md) — how each `status` is decided.
 - [docs/taxonomy.md](docs/taxonomy.md) — task/modality vocabulary; add new tokens to [`data/taxonomy.yml`](data/taxonomy.yml).
+## Automated Checks
+The catalog is machine-checked in CI on every push and pull request. These checks keep the README, [`data/resources.yml`](data/resources.yml), generated figures, CSV/JSON exports, and Hugging Face package consistent.
+You do not need to run local scripts before opening a PR. If CI reports a formatting or metadata issue, maintainers can help resolve it during review.
 ## Inclusion Policy

README.de.md CHANGED Viewed

@@ -18,9 +18,9 @@
 <h1 align="center">Awesome Egocentric Atlas</h1>
-<p align="center"><strong>Eine kuratierte Karte der First-Person-KI – die Datensätze, Benchmarks, Modelle und Werkzeuge hinter egozentrischem Sehen, verkörperter KI und Robotik, Video-Sprache, Langzeitgedächtnis, AR/VR und Hand-Objekt-Interaktion.</strong></p>
-<p align="center"><strong>277</strong> egozentrische Ressourcen — 104 Datensätze · 69 Benchmarks · 90 Modelle · 13 Toolkits</p>
 ## Inhalt

 <h1 align="center">Awesome Egocentric Atlas</h1>
+<p align="center"><strong>Eine kuratierte Karte egozentrischer KI – die Datensätze, Benchmarks, Modelle und Werkzeuge hinter egozentrischem Sehen, verkörperter KI und Robotik, Video-Sprache, Langzeitgedächtnis, AR/VR und Hand-Objekt-Interaktion.</strong></p>
+<p align="center"><strong>456</strong> egozentrische Ressourcen — 125 Datensätze · 81 Benchmarks · 226 Modelle · 23 Toolkits</p>
 ## Inhalt

README.es.md CHANGED Viewed

@@ -18,9 +18,9 @@
 <h1 align="center">Awesome Egocentric Atlas</h1>
-<p align="center"><strong>Un mapa curado de la IA en primera persona: los conjuntos de datos, benchmarks, modelos y herramientas tras la visión egocéntrica, la IA encarnada y la robótica, el aprendizaje visión-lenguaje, la memoria de largo contexto, la RA/RV y la interacción mano-objeto.</strong></p>
-<p align="center"><strong>277</strong> recursos egocéntricos — 104 conjuntos de datos · 69 benchmarks · 90 modelos · 13 herramientas</p>
 ## Qué incluye

 <h1 align="center">Awesome Egocentric Atlas</h1>
+<p align="center"><strong>Un mapa curado de la IA egocéntrica: los conjuntos de datos, benchmarks, modelos y herramientas tras la visión egocéntrica, la IA encarnada y la robótica, el aprendizaje visión-lenguaje, la memoria de largo contexto, la RA/RV y la interacción mano-objeto.</strong></p>
+<p align="center"><strong>456</strong> recursos egocéntricos — 125 conjuntos de datos · 81 benchmarks · 226 modelos · 23 herramientas</p>
 ## Qué incluye

README.fr.md CHANGED Viewed

@@ -18,9 +18,9 @@
 <h1 align="center">Awesome Egocentric Atlas</h1>
-<p align="center"><strong>Une carte sélective de l'IA à la première personne : les jeux de données, benchmarks, modèles et outils derrière la vision égocentrique, l'IA incarnée et la robotique, l'apprentissage vision-langage, la mémoire à long contexte, la RA/RV et l'interaction main-objet.</strong></p>
-<p align="center"><strong>277</strong> ressources égocentriques — 104 jeux de données · 69 benchmarks · 90 modèles · 13 outils</p>
 ## Contenu

 <h1 align="center">Awesome Egocentric Atlas</h1>
+<p align="center"><strong>Une carte sélective de l'IA égocentrique : les jeux de données, benchmarks, modèles et outils derrière la vision égocentrique, l'IA incarnée et la robotique, l'apprentissage vision-langage, la mémoire à long contexte, la RA/RV et l'interaction main-objet.</strong></p>
+<p align="center"><strong>456</strong> ressources égocentriques — 125 jeux de données · 81 benchmarks · 226 modèles · 23 outils</p>
 ## Contenu

README.ja.md CHANGED Viewed

@@ -18,9 +18,9 @@
 <h1 align="center">Awesome Egocentric Atlas</h1>
-<p align="center"><strong>一人称視点 AI の厳選マップ——エゴセントリック視覚、身体性 AI とロボティクス、ビデオ言語、長文脈記憶、AR/VR、手と物体の相互作用を支えるデータセット・ベンチマーク・モデル・ツールを収録。</strong></p>
-<p align="center"><strong>277</strong> エゴセントリック資源 — 104 データセット · 69 ベンチマーク · 90 モデル · 13 ツールキット</p>
 ## 収録内容

 <h1 align="center">Awesome Egocentric Atlas</h1>
+<p align="center"><strong>エゴセントリック AI の厳選マップ——エゴセントリック視覚、身体性 AI とロボティクス、ビデオ言語、長文脈記憶、AR/VR、手と物体の相互作用を支えるデータセット・ベンチマーク・モデル・ツールを収録。</strong></p>
+<p align="center"><strong>456</strong> エゴセントリック資源 — 125 データセット · 81 ベンチマーク · 226 モデル · 23 ツールキット</p>
 ## 収録内容

README.ko.md CHANGED Viewed

@@ -18,9 +18,9 @@
 <h1 align="center">Awesome Egocentric Atlas</h1>
-<p align="center"><strong>1인칭 AI의 엄선된 지도 — 자기중심 비전, 체화 AI와 로보틱스, 비디오-언어, 장문맥 기억, AR/VR, 손-물체 상호작용을 뒷받침하는 데이터셋·벤치마크·모델·도구를 담았습니다.</strong></p>
-<p align="center"><strong>277</strong> 자기중심 자원 — 104 데이터셋 · 69 벤치마크 · 90 모델 · 13 툴킷</p>
 ## 구성

 <h1 align="center">Awesome Egocentric Atlas</h1>
+<p align="center"><strong>자기중심 AI의 엄선된 지도 — 자기중심 비전, 체화 AI와 로보틱스, 비디오-언어, 장문맥 기억, AR/VR, 손-물체 상호작용을 뒷받침하는 데이터셋·벤치마크·모델·도구를 담았습니다.</strong></p>
+<p align="center"><strong>456</strong> 자기중심 자원 — 125 데이터셋 · 81 벤치마크 · 226 모델 · 23 툴킷</p>
 ## 구성

README.md CHANGED Viewed

@@ -70,7 +70,7 @@ A richer nested-JSON version (with lanes and summary stats) is in [`site-data.js
 </p>
 <p align="center">
-  <strong>A curated map of first-person AI — datasets, benchmarks, models, and tools.</strong>
 </p>
 <!-- LANG-BAR:START -->
@@ -149,7 +149,7 @@ Prefer a browsable view? The [interactive site](https://chaoyue0307.github.io/aw
 | 456 egocentric resources | 125 datasets, 81 benchmarks, 226 models, and 23 toolkits, plus a Project Aria collection hub — across vision, robotics, memory, and AR. Four related non-egocentric resources are listed separately. |
 | 6 research lanes | Foundation video, procedure/action, hands and 3D, memory/reasoning, robotics/VLA, and AR/wearable sensing. |
 | 5 access states | `open`, `request`, `benchmark`, `partial`, and `watch` keep availability visible before you plan experiments. |
-| Machine-checked catalog | [`data/resources.yml`](data/resources.yml) mirrors the README with type, year, status, URL, tasks, and provenance — and CI validates it on every change. |
 | Reader-first tables | Each entry is short enough to scan, then links out to the official page, paper, code, or dataset portal. |
 <p align="center">
@@ -158,7 +158,7 @@ Prefer a browsable view? The [interactive site](https://chaoyue0307.github.io/aw
 ## Milestones
-The landmark works that shaped egocentric AI — a fast on-ramp from the field's origins to its current frontier. Generated from entries tagged `milestone:` in [`data/resources.yml`](data/resources.yml).
 <p align="center">
   <img src="assets/awesome-egocentric-milestones.png" alt="Illustrated milestone timeline for representative egocentric AI works" width="100%">
@@ -166,26 +166,6 @@ The landmark works that shaped egocentric AI — a fast on-ramp from the field's
 <!-- MILESTONES:START (generated by build_artifacts.rb — do not edit by hand) -->
-| Date | Milestone | Why it matters |
-| :---: | :--- | :--- |
-| 2009 | [CMU-MMAC](http://kitchen.cs.cmu.edu/) | The earliest egocentric dataset; launched first-person activity recognition at the first IEEE Workshop on Egocentric Vision (CVPR 2009). |
-| 2011-06 | [GTEA / GTEA Gaze / EGTEA Gaze+](https://cbs.ic.gatech.edu/fpv/) | Foundational hand-object and gaze egocentric activity datasets (GTEA, CVPR 2011) that seeded first-person action and attention research. |
-| 2012 | [ADL Dataset](https://www.csc.kth.se/cvap/actions/) | Classic unscripted daily-life egocentric activity corpus with object and hand annotations; the reference point for ADL-scale first-person recognition. |
-| 2015 | [EgoHands](http://vision.soic.indiana.edu/projects/egohands/) | The Google Glass hand-segmentation benchmark that made hands a first-class signal for egocentric perception. |
-| 2020-06 | [EPIC-KITCHENS-100](https://epic-kitchens.github.io/) | The defining large-scale egocentric action-recognition benchmark and annual challenge suite. |
-| 2021-10 | [Ego4D](https://ego4d-data.org/) | The 3,670-hour massive-scale benchmark suite that catalyzed the modern egocentric era. |
-| 2022 | [Project Aria Datasets](https://www.projectaria.com/datasets/) | Meta's research smart-glasses platform that opened the modern wave of AR and wearable egocentric data. |
-| 2022-03 | [HOI4D](https://hoi4d.github.io/) | Large RGB-D 4D hand-object interaction dataset that moved egocentric HOI toward geometry, pose, and temporal scene understanding. |
-| 2022-06 | [EgoVLP](https://github.com/showlab/EgoVLP) | First egocentric video-language pretraining (EgoClip, EgoNCE) and a basis for ego representation learning. |
-| 2023-08 | [EgoSchema](http://egoschema.github.io/) | The benchmark that exposed how far models are from long-form egocentric video reasoning. |
-| 2023-11 | [Ego-Exo4D](https://ego-exo4d-data.org/) | Synchronized ego and exo skilled-activity capture at scale; the reference for cross-view egocentric learning. |
-| 2024-02 | [Universal Manipulation Interface / UMI](https://umi-gripper.github.io/) | Handheld/wrist-view manipulation interface that made robot-free in-the-wild demonstrations practical for cross-embodiment policy learning. |
-| 2024-06 | [HOT3D](https://facebookresearch.github.io/hot3d/) | Reference benchmark for 3D hand-object tracking from AR glasses (Project Aria and Quest 3). |
-| 2025-03 | [EgoLife](https://arxiv.org/abs/2503.03803) | Week-long Meta Aria daily-life corpus that pushed egocentric research toward personalized memory and life-assistant reasoning. |
-| 2025-07 | [EgoVLA](https://rchalyang.github.io/EgoVLA/) | Showed vision-language-action policies can be learned from egocentric human video and transferred to robots. |
-| 2026-02 | [EgoScale](https://arxiv.org/abs/2602.16710) | Revealed the log-linear data-scaling law for egocentric human-video VLA pretraining. |
-| 2026-03 | [Xperience-10M](https://huggingface.co/datasets/ropedia-ai/xperience-10m) | Petascale egocentric world-model corpus (10M experiences, ~1 PB) pushing first-person data to internet scale for embodied AI and robot learning. |
 <!-- MILESTONES:END -->
 ## Start Here
@@ -284,8 +264,8 @@ Broad, large-scale first-person corpora that most egocentric pipelines pretrain
 | [EgoCS-400K](https://arxiv.org/abs/2606.18180) | 2026-06 | arXiv | 400K+ first-person Counter-Strike gameplay videos (10K hours, 13 maps) with aligned actions, player state, camera motion, and game events | Action-conditioned interactive world models from first-person gameplay | watch |
 | [Ego-1K](https://huggingface.co/datasets/facebook/ego-1k) | 2026-03 | CVPR 2026 | Nearly 1,000 synchronized multiview egocentric videos from a custom 12-camera plus VR-headset rig | Dynamic 3D/4D scene understanding and novel view synthesis from ego rigs | open |
 | [EgoCrowds / CrowdEraser](https://arxiv.org/abs/2603.29036) | 2026-03 | arXiv | Semi-synthetic paired crowded/empty clips from real egocentric walking-tour video; CrowdEraser diffusion removes crowds for humanless walkthroughs | First-person walking-tour video editing and environment modeling | watch |
-| [Xperience-10M](https://huggingface.co/datasets/ropedia-ai/xperience-10m) | 2026 | Hugging Face | Ropedia release on Hugging Face; 10M experiences, 10K hours, six video streams, audio, stereo depth, camera pose, hand/body mocap, IMU, hierarchical language, ~1 PB total | Embodied AI, world models, robot learning from human experience, sensor fusion, 3D/4D understanding | request |
-| [Xperience-10M Sample](https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample) | 2026 | Hugging Face | Public sample episode for Xperience-10M with six rows on Hugging Face and `cc-by-nc-4.0` terms | Loader testing, demos, task-suite prototyping, annotation inspection | open |
 | [Look and Tell](https://arxiv.org/abs/2510.22672) | 2025-10 | NeurIPS 2025 Workshop | 25 participants, Project Aria plus stationary cameras, gaze/speech/video, 3D reconstructions | Referential communication across ego and exo viewpoints | watch |
 | [EgoBlind](https://arxiv.org/abs/2503.08221) | 2025-03 | NeurIPS 2025 D&B | 1,392 first-person videos from blind and visually impaired users, 5,311 questions posed or verified by blind users | Assistive egocentric VideoQA for blind users | watch |
 | [HD-EPIC](https://arxiv.org/abs/2502.04144) | 2025-02 | CVPR 2025 | 41 hours, 9 kitchens, dense fine-grained labels, 3D fixture annotations, audio events, VQA | Detailed kitchen understanding, VQA, 3D-aware ego reasoning, audio-event recognition | open |
@@ -297,7 +277,7 @@ Broad, large-scale first-person corpora that most egocentric pipelines pretrain
 | [Ego4D](https://ego4d-data.org/) | 2021-10 | CVPR 2022 | 3,670+ hours from 900+ camera wearers, multiple countries, video/audio/gaze/stereo/3D/narrations depending on subset | Long-form ego video, episodic memory, social, hand-object, forecasting, audio-visual tasks | request |
 | [EasyCom](https://arxiv.org/abs/2107.04174) | 2021-07 | arXiv | AR glasses egocentric multi-channel audio and wide-FOV RGB for noisy conversations | Speech enhancement, source localization, conversation assistance | open |
 | [EPIC-KITCHENS-100](https://epic-kitchens.github.io/) | 2020-06 | IJCV 2022 | 100 hours, 20M frames, 90K actions, 45 kitchens, narrations and dense action labels | Kitchen action recognition, anticipation, action detection, retrieval | open |
-| [EgoCom / Ego audio-visual correspondence](http://vision.cs.utexas.edu/projects/ego_av_corr/) | 2020 | project page | Egocentric video with spatial audio for conversation and audio-visual correspondence tasks | Active speaker detection, spatial audio denoising, conversational graph reasoning | open |
 | [From Third Person to First Person](https://arxiv.org/abs/1812.00104) | 2018-12 | CVPR 2019 | Ego/exo video synthesis and retrieval datasets plus baselines for bridging third-person social video to first-person views | Cross-view synthesis, retrieval, and ego-exo transfer | watch |
 | [EPIC-KITCHENS](https://epic-kitchens.github.io/) | 2018-04 | ECCV 2018 | 55 hours / 11.5M frames from 32 kitchens with narrations, 39.6K action segments, and object boxes | Original large-scale kitchen action recognition and anticipation benchmark | open |
@@ -324,7 +304,7 @@ Egocentric human-manipulation and wrist-camera data aimed at vision-language-act
 | [MV-UMI](https://arxiv.org/abs/2509.18757) | 2025-09 | arXiv | Multi-view UMI adds third-person context to wrist egocentric observations | Broad-scene context for cross-embodiment manipulation | watch |
 | [InterVLA](https://arxiv.org/abs/2508.04681) | 2025-08 | ICCV 2025 | 11.4 hours and 1.2M frames with 2 ego and 5 exo views, human/object motions, and verbal commands | Vision-language-action and motion estimation | watch |
 | [EgoDex](https://arxiv.org/abs/2505.11709) | 2025-05 | ICLR 2026 | 829 hours of Apple Vision Pro egocentric video, 194 tabletop tasks, 3D hand/finger tracking | Dexterous manipulation, imitation learning, human-to-robot hand trajectory prediction | watch |
-| [EgoVLA](https://rchalyang.github.io/EgoVLA/) | 2025 | project page | VLA training from egocentric human videos plus robot fine-tuning | Human-video-to-robot policy transfer | watch |
 | [FastUMI](https://arxiv.org/abs/2409.19499) | 2024-09 | arXiv | UMI redesign reporting 10K+ real-world trajectories across 22 everyday tasks | Faster hardware-independent UMI-style collection | watch |
 | [EgoPAT3D / EgoPAT3Dv2](https://arxiv.org/abs/2403.05046) | 2024-03 | ICRA 2024 | 1M+ RGB-D/IMU frames in the original task; v2 expands egocentric 3D action-target prediction | Human-robot interaction, 3D target anticipation, manipulation safety | open |
 | [Universal Manipulation Interface / UMI](https://umi-gripper.github.io/) | 2024-02 | RSS 2024 | Hand-held GoPro gripper interface and policy stack for in-the-wild robot teaching | Wrist-view robot-free demonstrations and cross-embodiment policy transfer | open |
@@ -346,6 +326,7 @@ Fine-grained hand, object, contact, and 3D-pose datasets, including emerging eve
 | [EgoFun3D](https://arxiv.org/abs/2604.11038) | 2026-04 | arXiv | 271 egocentric videos with 3D geometry, part segmentation, articulation and function-template annotations | Interactive 3D object modeling from ego video | watch |
 | [SHOW3D](https://arxiv.org/abs/2603.28760) | 2026-03 | CVPR 2026 | In-the-wild 3D hand-object interactions from a back-mounted multi-camera rig synced to a worn VR headset, with multi-view 3D shape/pose and text | In-the-wild 3D hand-object reconstruction | watch |
 | [EgoXtreme](https://arxiv.org/abs/2603.25135) | 2026-03 | CVPR 2026 | Smart-glasses egocentric 6D object-pose dataset across industrial, sports, and rescue scenes with extreme motion blur, dynamic lighting, and occlusion | Robust 6D object pose under extreme egocentric conditions | watch |
 | [Eva-3M / EvaPose](https://arxiv.org/abs/2602.23618) | 2026-02 | CVPR 2026 | 3.0M+ egocentric HPE frames, including 435K keypoint-visibility labels, plus visibility-aware pose estimation | Visibility-aware egocentric human pose | watch |
 | [WristPP](https://zhenqis123.github.io/WristPP/) | 2026-02 | CHI 2026 submission | Wrist-worn wide-FOV RGB system with a 133K-frame pose-pressure dataset from 20 subjects | Mobile hand pose and pressure interaction | watch |
 | [OpenTouch](https://opentouch-tactile.github.io/) | 2025-12 | arXiv | 5.1 hours of synchronized egocentric video-touch-pose data, 2,900 curated clips, text annotations, and retrieval/classification benchmarks | Full-hand tactile grounding for real-world interaction | watch |
@@ -355,7 +336,7 @@ Fine-grained hand, object, contact, and 3D-pose datasets, including emerging eve
 | [EgoPressure](https://yiming-zhao.github.io/EgoPressure/) | 2024-09 | CVPR 2025 | 5.0 hours, 21 participants, a moving egocentric camera plus 7 stationary RGB-D cameras, hand pose meshes, and fine-grained per-contact touch pressure (CVPR 2025) | Hand pressure and pose from egocentric vision | open |
 | [HOT3D](https://facebookresearch.github.io/hot3d/) | 2024-06 | CVPR 2025 | 833+ minutes, 3.7M+ images, Aria and Quest 3, gaze, point clouds, 3D hand/object/camera poses | 3D hand-object tracking for AR/VR manipulation | open |
 | [UnrealEgo2 / UnrealEgo-RW](https://arxiv.org/abs/2401.00889) | 2024-01 | arXiv | Expanded stereo egocentric pose datasets from synthetic and real-world capture | Stereo egocentric human pose estimation | watch |
-| [EgoEVHands](https://github.com/ZJUWang01/EgoEV-HandPose) | 2024 | GitHub | Stereo event-camera egocentric hand dataset with 5,419 sequences and 3D/2D keypoints | Event-based bimanual hand pose and gesture recognition | watch |
 | [POV-Surgery](https://batfacewayne.github.io/POV_Surgery_io/) | 2023-07 | MICCAI 2023 | Egocentric surgical video for hand and tool pose estimation during operating-room activities | Surgical hand-tool pose and interaction understanding | open |
 | [EgoHumans](https://arxiv.org/abs/2305.16487) | 2023-05 | ICCV 2023 | 125K+ egocentric images for in-the-wild multi-human tracking, 2D/3D pose, and mesh recovery | Egocentric multi-human perception and tracking | partial |
 | [EgoTracks](https://arxiv.org/abs/2301.03213) | 2023-01 | NeurIPS 2023 | Long-term egocentric visual object tracking annotations from Ego4D | Tracking, re-detection, embodied object persistence | open |
@@ -366,16 +347,16 @@ Fine-grained hand, object, contact, and 3D-pose datasets, including emerging eve
 | [ARCTIC](https://arctic.is.tue.mpg.de/) | 2022-04 | CVPR 2023 | 2.1M frames with bimanual hand/object meshes, articulated objects, and contact | Dexterous bimanual manipulation, contact, reconstruction | request |
 | [GIMO](https://github.com/y-zheng18/GIMO) | 2022-04 | ECCV 2022 | Ego-centric views, gaze, scene scans, and high-quality body pose sequences | Gaze-informed human motion prediction in scene context | open |
 | [HOI4D](https://hoi4d.github.io/) | 2022-03 | CVPR 2022 | 2.4M RGB-D egocentric frames, 4,000+ sequences, 800 objects, 16 categories, 610 rooms | 4D human-object interaction, segmentation, object pose tracking, action segmentation | open |
-| [EgoBody](https://sanweiliti.github.io/egobody/egobody.html) | 2022 | ECCV 2022 | HoloLens2 egocentric RGB/depth/eye/head/hand data with 3D body pose and shape | Egocentric human pose, shape, motion, social interaction | open |
 | [H2O: Two Hands and Objects](https://arxiv.org/abs/2104.11181) | 2021-04 | ICCV 2021 | Synchronized multi-view RGB-D, two-hand 3D pose, 6D object pose, object meshes, camera poses | First-person two-hand interaction recognition and pose-aware HOI | open |
-| [TREK-150](https://machinelearning.uniud.it/datasets/trek150/) | 2021 | project page | 150 egocentric tracking sequences derived from EPIC-KITCHENS | Single-object tracking in egocentric video | open |
 | [Ego2Hands](https://arxiv.org/abs/2011.07252) | 2020-11 | arXiv | Large-scale composited RGB egocentric two-hand segmentation/detection | Robust unconstrained hand segmentation | open |
-| [xR-EgoPose](https://github.com/facebookresearch/xR-EgoPose) | 2019 | GitHub | Synthetic egocentric body pose data for XR headset viewpoints | 3D human pose from head-mounted cameras | open |
 | [EgoYouTubeHands / HandOverFace](https://arxiv.org/abs/1803.03317) | 2018-03 | CVPR 2018 | In-the-wild egocentric hand-segmentation study built around EgoYouTubeHands, HandOverFace, EGTEA, and EgoHands-style supervision | Hand detection and segmentation beyond lab capture | watch |
-| [FPHA / First-Person Hand Action](https://guiggh.github.io/publications/first-person-hands/) | 2017 | CVPR 2018 | 100K+ RGB-D frames, 45 action classes, 26 objects, 3D hand/object poses | 3D hand pose and first-person hand action recognition | open |
 | [EgoGesture](https://ieeexplore.ieee.org/document/8299578/) | 2017 | IEEE TMM 2018 | 24K+ gesture samples, 3M RGB-D frames, 50 subjects, 83 static and dynamic gestures across six scenes | First-person gesture recognition for wearable interaction | open |
-| [EgoHands](http://vision.soic.indiana.edu/projects/egohands/) | 2015 | ICCV 2015 | 48 Google Glass videos, 4,800 annotated hand images | Hand detection and segmentation | open |
-| [BEOID](https://dimadamen.github.io/BEOID/) | 2014 | BMVC 2014 | Gaze-tracked egocentric video of 8 users interacting with objects across 6 everyday locations (kitchen, workspace, printer, corridor, gym) | Discovering task-relevant objects and interaction modes | open |
 ### Daily Life, Memory, Assistance, and QA
@@ -412,7 +393,7 @@ Long-horizon daily-life capture and the question-answering benchmarks that probe
 | [Wearable Product Localization](https://arxiv.org/abs/2601.12486) | 2026-01 | arXiv | Wearable assistive shopping system combining detection, VLM guidance, spatialized sonification, and corrective feedback | Product search and navigation for blind or low-vision users | watch |
 | [Egocentric Clinical Intent](https://arxiv.org/abs/2601.06750) | 2026-01 | arXiv | Benchmark for egocentric clinical intent understanding by medical multimodal LLMs over first-person clinical procedure video | Clinical intent reasoning for medical assistants | watch |
 | [WearVox](https://arxiv.org/abs/2601.02391) | 2026-01 | arXiv | Egocentric multichannel voice-assistant benchmark for spoken interaction grounded in first-person audio-visual context | Wearable voice-assistant evaluation | watch |
-| [EgoMemory](https://openreview.net/forum?id=T0em4hJCQb) | 2026 | OpenReview | 165,795 user-specific object annotations over 245 videos from 45 participants | Memory-augmented personalized retrieval | watch |
 | [Ego-EXTRA](https://fpv-iplab.github.io/Ego-EXTRA/) | 2025-12 | arXiv | 50 hours of expert-trainee procedural assistance dialogue, 15K+ VQA pairs | Egocentric video-language assistants and expert feedback | open |
 | [WearVQA](https://arxiv.org/abs/2511.22154) | 2025-11 | NeurIPS 2025 | 2,520 image-question-answer triplets across 7 domains and 10 task types under occluded/low-light/blurry wearable capture (NeurIPS 2025) | Smart-glasses VQA under real wearable conditions | watch |
 | [TeleEgo](https://arxiv.org/abs/2510.23981) | 2025-10 | arXiv | Streaming omni-modal benchmark with 3,291 QA items across memory, understanding, and cross-memory reasoning | Real-time egocentric AI assistant evaluation | watch |
@@ -425,16 +406,16 @@ Long-horizon daily-life capture and the question-answering benchmarks that probe
 | [EgoLife](https://arxiv.org/abs/2503.03803) | 2025-03 | CVPR 2025 | 300 hours from six participants over one week with Meta Aria, third-person references, and EgoLifeQA | Long-term daily-life memory, personalized assistants, ultra-long QA | partial |
 | [EgoToM](https://arxiv.org/abs/2503.22152) | 2025-03 | arXiv | Theory-of-mind QA over Ego4D-style egocentric videos | Goals, beliefs, and next-action reasoning for camera wearers | watch |
 | [X-LeBench](https://arxiv.org/abs/2501.06835) | 2025-01 | arXiv | 432 simulated life logs over Ego4D-like footage, spanning 23 minutes to 16.4 hours | Extremely long egocentric video understanding | watch |
-| [EgoSelf](https://abie-e.github.io/egoself_project/) | 2025 | project page | Personalized egocentric assistant framework with graph memory | Personalization from long-term egocentric interaction memory | watch |
-| [EgoCross](https://github.com/MyUniverse0726/EgoCross) | 2025 | GitHub | About 1,000 QA pairs over surgery, industry, extreme sports, and animal-perspective clips | Cross-domain egocentric QA generalization | watch |
-| [EgoTextVQA](https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_EgoTextVQA_Towards_Egocentric_Scene-Text_Aware_Video_Question_Answering_CVPR_2025_paper.pdf) | 2025 | CVPR 2025 | Egocentric scene-text-aware video QA across housekeeping and driving scenes (CVPR 2025) | Reading and reasoning over first-person scene text | open |
 | [VidEgoThink](https://arxiv.org/abs/2410.11623) | 2024-10 | ICLR 2025 | Ego4D-based benchmark for video QA, hierarchy planning, visual grounding, and reward modeling | Embodied egocentric video understanding | benchmark |
 | [EgoThink](https://arxiv.org/abs/2311.15596) | 2023-11 | CVPR 2024 | First-person VQA benchmark covering six capability groups and twelve dimensions | First-person perspective reasoning for VLMs | benchmark |
 | [EgoSchema](http://egoschema.github.io/) | 2023-08 | NeurIPS 2023 | 5K+ curated multiple-choice QA pairs over 250+ hours from Ego4D clips | Very long-form video-language understanding | benchmark |
 | [EgoTaskQA](https://arxiv.org/abs/2210.03929) | 2022-10 | NeurIPS 2022 | Diagnostic QA benchmark for task dependencies, effects, intents, beliefs, and counterfactuals in ego video | Task-step reasoning and procedural QA | benchmark |
 | [AssistQ](https://showlab.github.io/assistq/) | 2022-03 | ECCV 2022 | 531 question-answer samples from 100 newly filmed instructional videos | Assistance-oriented video QA and affordance-centric task completion | open |
 | [MMAC Captions](https://arxiv.org/abs/2109.02955) | 2021-09 | ACM MM 2021 | Sensor-augmented egocentric-video captioning data around CMU-MMAC-style multimodal activity streams | Video captioning with RGB, audio, IMU, and text | watch |
-| [EgoVQA](https://openaccess.thecvf.com/content_ICCVW_2019/html/EPIC/Fan_EgoVQA_-_An_Egocentric_Video_Question_Answering_Benchmark_Dataset_ICCVW_2019_paper.html) | 2019 | ICCV 2019 | 600+ QA pairs over egocentric videos; an early first-person VideoQA benchmark | Classic egocentric VideoQA | open |
 | [First-Person Stories](https://arxiv.org/abs/1707.07863) | 2017-07 | ICIAP 2017 Workshop | 45K+ egocentric photo-stream images labeled for lifestyle patterns such as eating, socializing, and sedentary behavior | Lifelogging and daily-life behavior analysis | watch |
 ### Action, Procedure, Lifelogging, and Classic FPV
@@ -444,6 +425,7 @@ Procedural and activity datasets, from modern industrial assembly to the classic
 | Resource | Released | Venue | Scale / signal | Best for | Status |
 | :--- | :---: | :---: | :--- | :--- | :---: |
 | [EgoMAGIC](https://arxiv.org/abs/2604.22036) | 2026-04 | arXiv | 3,355 egocentric field-medicine videos over 50 tasks from a head-mounted stereo camera with audio; 1.95M labels, 124 objects, action-detection challenge (Zenodo) | Field-medicine perception, action and object detection | open |
 | [ENIGMA-360](https://arxiv.org/abs/2603.09741) | 2026-03 | arXiv | Ego-exo dataset for human behavior understanding in industrial scenarios with 360-degree and egocentric capture | Industrial ego-exo behavior understanding | watch |
 | [PEDESTRIAN](https://arxiv.org/abs/2512.19190) | 2025-12 | arXiv | 340 first-person pavement videos covering 29 urban sidewalk obstacle types, with deep-learning detection baselines | Pedestrian-safety obstacle detection from first-person video | watch |
 | [IndEgo](https://huggingface.co/datasets/FraunhoferIPK/IndEgo) | 2025-11 | NeurIPS 2025 | ~197h egocentric (plus ~97h exocentric) industrial collaborative work over assembly, logistics, inspection, and repair; gaze, narration, sound, motion, hand pose, point clouds | Industrial egocentric assistants and procedure understanding | open |
@@ -452,14 +434,14 @@ Procedural and activity datasets, from modern industrial assembly to the classic
 | [EgoMe](https://arxiv.org/abs/2501.19061) | 2025-01 | arXiv | Real-world following-me dataset pairing exocentric demonstrations with egocentric imitation across everyday tasks | Egocentric imitation and cross-view following | watch |
 | [EgoExo-Fitness](https://github.com/iSEE-Laboratory/EgoExo-Fitness) | 2024-06 | ECCV 2024 | Synchronized ego and exo fitness videos with keypoint verification, execution comments, and quality scores (ECCV 2024) | Ego-exo full-body action quality and skill assessment | open |
 | [EgoExoLearn](https://github.com/OpenGVLab/EgoExoLearn) | 2024-03 | CVPR 2024 | 120 hours of egocentric plus demonstration videos with gaze and annotations | Bridging asynchronous ego and exo procedural activity | open |
-| [EgoSurgery](https://github.com/Fujiry0/EgoSurgery) | 2024 | MICCAI 2024 | EgoSurgery-Phase (surgical phase recognition) and EgoSurgery-HTS (pixel-wise hand-tool segmentation of 14 tools) from egocentric open-surgery video (MICCAI 2024) | Surgical phase, hand, and tool understanding | open |
 | [CaptainCook4D](https://arxiv.org/abs/2312.14556) | 2023-12 | NeurIPS 2024 D&B | Procedural-activity dataset for understanding execution errors, later reused in multimodal temporal-action-segmentation work | Procedural errors and mistake-aware activity understanding | watch |
 | [IndustReal](https://arxiv.org/abs/2310.17323) | 2023-10 | WACV 2024 | Industrial-like egocentric procedure-step recognition dataset with execution errors | Industrial procedure recognition and error handling | watch |
 | [EGOFALLS](https://arxiv.org/abs/2309.04579) | 2023-09 | arXiv | Visual-audio egocentric dataset and benchmark for fall detection | Wearable fall detection and safety monitoring | watch |
 | [WEAR](https://mariusbock.github.io/wear/) | 2023-04 | IMWUT 2024 | 22 participants, 18 outdoor workouts, synchronized egocentric video and 3D acceleration across 11 locations (IMWUT 2024) | Vision-plus-inertial outdoor activity recognition | open |
-| [Visual Experience Dataset / VEDB](http://tamaraberg.com/visualexperience/) | 2023 | project page | 240+ hours egocentric video with gaze/head tracking in classic literature | Lifelogging, attention modeling, visual experience statistics | partial |
 | [EgoProceL](https://sid2697.github.io/egoprocel/) | 2022-07 | ECCV 2022 | 62 hours, 130 subjects, 16 procedural tasks | Key-step localization and procedure learning from ego videos | open |
-| [FT-HID](https://github.com/ENDLICHERE/FT-HID) | 2022 | GitHub | 90K+ RGB-D first- and third-person human interaction samples from 109 subjects | FPV/TPV aligned human interaction analysis | open |
 | [KrishnaCam / OAK](https://oakdata.github.io/) | 2021-08 | ICCV 2021 | Long-running Google Glass daily-life stream; OAK adds 17.5 hours of object annotations | Continual object detection, summarization, personal visual memory | partial |
 | [Home Action Genome / HOMAGE](https://homeactiongenome.org/) | 2021-05 | CVPR 2021 | 27 participants, synchronized ego and third-person views, 12 sensor types, hierarchical activity/action labels | Compositional multi-view home activity understanding | open |
 | [MECCANO](https://iplab.dmi.unict.it/MECCANO/) | 2020-10 | WACV 2021 | Industrial-like motorbike model assembly with RGB/depth/gaze variants | EHOI, active object detection, anticipation, industrial procedures | open |
@@ -473,13 +455,13 @@ Procedural and activity datasets, from modern industrial assembly to the classic
 | [Ego-Engagement](https://arxiv.org/abs/1604.00906) | 2016-04 | arXiv | Egocentric video dataset and model for whether the wearer is engaged with people or objects | Engagement, attention, and object/person interaction cues | watch |
 | [Wrist-mounted ADL](https://arxiv.org/abs/1511.06783) | 2015-11 | CVPR 2016 | Synchronized head and wrist wearable-camera daily activities | Comparing head- versus wrist-mounted first-person views | open |
 | [Ego-Object Discovery / EDUB](https://arxiv.org/abs/1504.01639) | 2015-04 | arXiv | 4,912 egocentric daily-life photo-stream images from four users for unsupervised object discovery | Lifelog object discovery and detection | open |
-| [HUJI EgoSeg](https://www.vision.huji.ac.il/egoseg/) | 2014 | project page | Long egocentric videos for temporal segmentation | Egocentric event segmentation | partial |
 | [DogCentric](https://robotics.ait.kyushu-u.ac.jp/dog-centric-activity-dataset/) | 2014 | project page | Dog-mounted first-person activity videos | Animal egocentric activity recognition | open |
 | [JPL First-Person Interaction](https://ieeexplore.ieee.org/document/6909626) | 2013 | IEEE | First-person videos of people interacting with a humanoid observer | Human interaction recognition from first person | partial |
-| [First-Person Social Interactions](http://ai.stanford.edu/~alireza/publication/CVPR12.pdf) | 2012 | CVPR 2012 | Day-long head-mounted video of 8 subjects at a theme park, annotated for social interactions, roles, attention, and turn-taking | First egocentric social-interaction dataset | open |
-| [ADL Dataset](https://www.csc.kth.se/cvap/actions/) | 2012 | project page | Unscripted daily activity recordings with activity/object/hand annotations in classic literature | Daily living action recognition and object interaction | partial |
-| [UT Ego](http://vision.cs.utexas.edu/projects/egocentric/) | 2012 | project page | Long daily egocentric videos in classic summarization work | Temporal segmentation and summarization | partial |
-| [CMU-MMAC](http://kitchen.cs.cmu.edu/) | 2009 | CMU tech report 2009 | Multimodal kitchen-activity database: head-mounted egocentric video plus body IMUs, motion capture, and audio for 43 subjects cooking 5 recipes | One of the first egocentric activity datasets | open |
 ### Project Aria, AR/VR, and 3D Scene Resources
@@ -500,7 +482,7 @@ AR-glasses and headset data with gaze, SLAM, and digital-twin annotations for sc
 | [Aria Everyday Activities / AEA](https://www.projectaria.com/datasets/aea/) | 2024-02 | arXiv | 143 daily activity sequences across five indoor locations with trajectories, point clouds, gaze, speech | Everyday AR perception, scene reconstruction, prompted segmentation | open |
 | [SANPO](https://arxiv.org/abs/2309.12172) | 2023-09 | WACV 2025 | Scene understanding, accessibility, and human navigation data for egocentric navigation and spatial assistance | Navigation and assistive scene understanding | watch |
 | [Aria Digital Twin / ADT](https://www.projectaria.com/datasets/adt/) | 2023-06 | arXiv | 200 real-world activity sequences, raw Aria streams, 6DoF poses, object poses, depth, segmentation, synthetic renderings | Egocentric 3D machine perception and digital-twin evaluation | open |
-| [Project Aria Datasets](https://www.projectaria.com/datasets/) | 2022 | project page | Official portal for Aria-based datasets and tooling | AR glasses, wearable sensing, scene reconstruction, gaze, SLAM | open |
 ## Benchmarks and Derived Annotations
@@ -515,10 +497,12 @@ Evaluation suites and label sets built on top of the raw datasets above.
 | [VL-MemKnG / WalkieKnowledgeT+](https://arxiv.org/abs/2606.17183) | 2026-06 | arXiv | Long egocentric navigation trajectories | Temporally distributed spatial-memory QA with hybrid graph and segment retrieval | watch |
 | [Plan, Watch, Recover / EgoProactive](https://arxiv.org/abs/2606.04970) | 2026-06 | arXiv | EgoProactive plus Pro2Bench over five established benchmarks | Proactive procedural assistance, out-of-plan detection, and recovery guidance | watch |
 | [EgoBench](https://arxiv.org/abs/2605.27820) | 2026-05 | arXiv | Egocentric video tasks | Interactive multimodal tool-using agents | watch |
 | [Ego-METAS](https://maria-sanvil.github.io/Ego-METAS-website/) | 2026-05 | arXiv | EgoExo4D, CMU-MMAC, CaptainCook4D | Online multimodal, energy-aware temporal action segmentation across RGB, audio, gaze, IMU, and monochrome streams | watch |
 | [EgoProx](https://arxiv.org/abs/2605.24456) | 2026-05 | CVPR 2026 | Egocentric 3D proximity QA | Intention, exploration, exploitation, and chain-of-actions spatial reasoning for MLLMs | watch |
 | [BARISTA](https://arxiv.org/abs/2605.12074) | 2026-05 | arXiv | 185 coffee-preparation videos | Scene graphs, masks, tracks, boxes, hand-object interactions, activities, and process-step reasoning | watch |
 | [TAVIS](https://arxiv.org/abs/2605.07943) | 2026-05 | arXiv | IsaacLab active-vision imitation tasks | Headcam vs fixed-cam evaluation, wrist/head active vision, and anticipatory-gaze metric | watch |
 | [EgoExoMem](https://arxiv.org/abs/2605.18734) | 2026-05 | arXiv | Synchronized ego-exo videos | Cross-view memory QA | watch |
 | [EgoMemReason](https://arxiv.org/abs/2605.09874) | 2026-05 | arXiv | Week-long egocentric video | Entity, event, and behavior memory reasoning | watch |
 | [Ego2World](https://arxiv.org/abs/2605.13335) | 2026-05 | arXiv | HD-EPIC | Executable symbolic worlds from egocentric cooking video for belief-state planning | watch |
@@ -530,30 +514,31 @@ Evaluation suites and label sets built on top of the raw datasets above.
 | [MA-EgoQA](https://ma-egoqa.github.io/) | 2026-03 | arXiv | Multi-agent egocentric streams | Social, task coordination, theory-of-mind, temporal, environment QA | open |
 | [EgoAVU](https://github.com/facebookresearch/EgoAVU) | 2026-02 | CVPR 2026 | Egocentric audio-visual narrations | EgoAVU-Instruct (3M QAs) and EgoAVU-Bench (3K QAs) for audio-visual understanding (CVPR 2026 highlight) | open |
 | [SAW-Bench](https://arxiv.org/abs/2602.16682) | 2026-02 | arXiv | Ray-Ban Meta smart-glasses video | Observer-centric situated awareness and physically grounded spatial reasoning | watch |
 | [Sanpo-D](https://arxiv.org/abs/2601.18100) | 2026-01 | arXiv | Sanpo egocentric navigation video | Spatial-conditioned reasoning over long first-person videos with fine-grained spatial re-annotation | watch |
-| [Ropedia Xperience-10M Task Suite](https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite) | 2026 | Hugging Face | Xperience-10M Sample | 12 embodied-AI task contracts, sample baselines, and evaluation protocol | open |
 | [EgoExo-Con](https://arxiv.org/abs/2510.26113) | 2025-10 | arXiv | Synchronized ego-exo videos | View-invariant temporal verification and grounding; introduces View-GRPO | watch |
 | [LaMAria](https://lamaria.ethz.ch) | 2025-09 | ICCV 2025 | Glasses-like wearable capture | City-scale egocentric visual-inertial SLAM with centimeter ground truth | open |
 | [EgoIllusion](https://arxiv.org/abs/2508.12687) | 2025-08 | arXiv | Egocentric video | Hallucination benchmark probing fabricated objects, actions, and sounds in multimodal models | watch |
 | [EgoExoBench](https://arxiv.org/abs/2507.18342) | 2025-07 | arXiv | Public ego-exo video datasets | 7,300+ QA pairs over semantic alignment, viewpoint association, and temporal reasoning | watch |
 | [EASG-Bench](https://github.com/fpv-iplab/EASG-bench) | 2025-06 | ICCV 2025 Workshop | Egocentric action scene graphs | Relation and temporal QA | open |
 | [HD-EPIC VQA Challenge](https://arxiv.org/abs/2502.04144) | 2025-02 | CVPR 2025 | HD-EPIC | Recipe, ingredient, nutrition, fine-grained action, 3D perception, object motion, gaze | benchmark |
-| [EgoCross](https://github.com/MyUniverse0726/EgoCross) | 2025 | GitHub | Cross-domain ego clips | Surgery, industry, extreme sports, animal-perspective QA | watch |
 | [EgoHOIBench / EgoNCE++](https://github.com/xuboshen/EgoNCEpp) | 2024-05 | ICLR 2025 | Multiple egocentric HOI sources | Open-vocabulary hand-object interaction understanding | open |
 | [EgoPlan-Bench](https://github.com/ChenYi99/EgoPlan) | 2023-12 | arXiv | Egocentric planning tasks | Multimodal LLM planning over human-level egocentric scenarios | open |
 | [Egocentric Pedestrian Trajectory Benchmark](https://arxiv.org/abs/2310.10424) | 2023-10 | arXiv | Egocentric pedestrian video | Trajectory prediction with scale- and motion-aware evaluation | watch |
 | [RefEgo](https://github.com/shuheikurita/RefEgo) | 2023-08 | ICCV 2023 | Ego4D | 12K+ clips / 41 hours for first-person referring-expression comprehension and referred-object tracking | open |
 | [EgoSchema](http://egoschema.github.io/) | 2023-08 | NeurIPS 2023 | Ego4D | Very-long-form multiple-choice video QA | open |
 | [Fine-Grained Affordance Annotation](https://arxiv.org/abs/2302.03292) | 2023-02 | WACV 2023 | Egocentric HOI videos | Fine-grained affordance labels for hand-object interaction | watch |
-| [Ego-Exo4D Benchmarks](https://ego-exo4d-data.org/) | 2023 | project page | Ego-Exo4D | Fine-grained activity, proficiency, cross-view translation, 3D pose, object correspondence | benchmark |
-| [EPIC-Sounds](https://epic-kitchens.github.io/epic-sounds/) | 2023 | ICASSP 2023 | EPIC-KITCHENS | Audio event recognition in egocentric kitchen video | open |
-| [EPIC-Fields](https://epic-kitchens.github.io/epic-fields/) | 2023 | NeurIPS 2023 | EPIC-KITCHENS | 3D fields and scene-level spatial reasoning over kitchen video | open |
 | [EgoClip / EgoMCQ](https://github.com/showlab/EgoVLP) | 2022-06 | NeurIPS 2022 | Ego4D | 3.8M clip-text pairs and MCQ development benchmark for egocentric VLP | open |
-| [VISOR](https://epic-kitchens.github.io/VISOR/) | 2022 | NeurIPS 2022 | EPIC-KITCHENS | Manual and dense masks, hand/object segmentation, active object relations | open |
 | [AssistSR](https://arxiv.org/abs/2111.15050) | 2021-11 | arXiv | Instructional daily-item video segments | Task-oriented question-driven video segment retrieval for personal assistants | watch |
-| [Ego4D Benchmarks](https://ego4d-data.org/) | 2021 | project page | Ego4D | Natural Language Query, Moment Query, episodic memory, state change, long-term anticipation, social/audio, hand-object | benchmark |
-| [TREK-150](https://machinelearning.uniud.it/datasets/trek150/) | 2021 | project page | EPIC-KITCHENS | Egocentric single-object tracking | open |
-| [EPIC-KITCHENS Challenges](https://epic-kitchens.github.io/) | 2018 | project page | EPIC-KITCHENS / EPIC-KITCHENS-100 | Recognition, detection, anticipation, retrieval, domain adaptation | benchmark |
 ## Models, Tools, and Baselines
@@ -564,6 +549,10 @@ Open models, baselines, and loaders you can build on directly.
 | Resource | Released | Venue | What it contributes | Link |
 | :--- | :---: | :---: | :--- | :---: |
 | UNIEGO | 2026-06 | arXiv | Unified egocentric encoder distilled from nine teachers spanning ego-exo views, RGB, depth, skeleton, and foundation-model representations | [Paper](https://arxiv.org/abs/2606.20559) |
 | PhysBrain | 2025-12 | arXiv | Uses human egocentric data to bridge vision-language models toward physical intelligence and embodied control | [Paper](https://arxiv.org/abs/2512.16793) |
 | EgoM2P | 2025-06 | ICCV 2025 | Egocentric multimodal multitask pretraining over RGB, depth, gaze, and camera pose | [Paper](https://arxiv.org/abs/2506.07886) |
 | Exo2Ego / Ego-ExoClip | 2025-03 | AAAI 2026 | Transfers exocentric MLLM knowledge into egocentric video understanding with 1.1M synchronized ego-exo clip-text pairs and EgoIT instruction tuning | [Paper](https://arxiv.org/abs/2503.09143) |
@@ -575,80 +564,49 @@ Open models, baselines, and loaders you can build on directly.
 | EgoDistill | 2023-01 | tech report | Distills heavy egocentric clip features into efficient models using head-motion signals | [Project](https://vision.cs.utexas.edu/projects/egodistill/) |
 | EgoT2 / Egocentric Video Task Translation | 2022-12 | CVPR 2023 Highlight | Task-translation framework that transfers supervision between egocentric video tasks | [Project](https://vision.cs.utexas.edu/projects/egot2/) |
-### Latest 2026 Scan Additions
-Fresh entries from the June 2026 source scan. Most are marked `watch` until code, data, or challenge artifacts are stable.
 | Resource | Released | Venue | What it contributes | Link |
 | :--- | :---: | :---: | :--- | :---: |
-| ActiveMimic | 2026-06 | arXiv | Egocentric human-video pretraining with active-perception signals for manipulation and VLA transfer | [Paper](https://arxiv.org/abs/2606.06194) |
 | AnchorWorld | 2026-06 | arXiv | Embodied egocentric world simulation with view-based evolution customization | [Paper](https://arxiv.org/abs/2606.07326) |
-| Continual Child-View Learning | 2026-06 | arXiv | Chronological multimodal learning from a child's egocentric video and speech stream | [Paper](https://arxiv.org/abs/2606.05115) |
-| EgoPriMo | 2026-06 | arXiv | Egocentric motion prior for interactive humanoid control from human demonstrations | [Paper](https://arxiv.org/abs/2606.08495) |
-| Objects Before Words | 2026-06 | arXiv | Object-first language grounding from child-view egocentric video | [Paper](https://arxiv.org/abs/2606.12985) |
-| OpenGlass | 2026-06 | arXiv | Low-power open AI eyewear platform with event-based vision support | [Paper](https://arxiv.org/abs/2606.07431) |
 | Understanding-Enhanced Ego Mistake Detection | 2026-06 | arXiv | Small/large-model collaboration for detecting incorrect procedural actions in egocentric video | [Paper](https://arxiv.org/abs/2606.02120) |
-| Watch Remember Reason | 2026-06 | arXiv | Human-view long-video understanding framework for MLLM watching, memory, and reasoning | [Paper](https://arxiv.org/abs/2606.07433) |
-| Beyond Motion Primitives | 2026-05 | arXiv | Ego4D-derived head-mounted IMU benchmark for behavioral activity recognition on smart glasses | [Paper](https://arxiv.org/abs/2605.27464) |
 | CASTLE2026 Team WDL | 2026-05 | CVPR 2026 EgoVis | Evidence-aware multimodal reasoning pipeline for long-form CASTLE egocentric QA | [Paper](https://arxiv.org/abs/2606.00712) |
 | CuriosAI CASTLE | 2026-05 | CVPR 2026 EgoVis | Search-verify-answer CASTLE challenge pipeline using timelines, transcripts, and VLM captions | [Paper](https://arxiv.org/abs/2605.27800) |
 | EgoCross Domain-Wise Inference | 2026-05 | CVPR 2026 EgoVis | Nearly training-free source-limited inference strategy for EgoCross domain shift | [Paper](https://arxiv.org/abs/2606.00829) |
-| EgoRelight | 2026-05 | arXiv | HMD-based egocentric human capture and illumination recovery for relightable avatars | [Paper](https://arxiv.org/abs/2605.28401) |
-| FROST-STA | 2026-05 | CVPR 2026 EgoVis | Frozen dense-feature Ego4D short-term object-interaction anticipation submission | [Paper](https://arxiv.org/abs/2606.00694) |
-| GBAT | 2026-05 | arXiv | Toolkit for annotating egocentric eye-tracking and video in child-caregiver interaction | [Paper](https://arxiv.org/abs/2605.22962) |
 | HD-EPIC Semantic-Visual Evidence | 2026-05 | CVPR 2026 EgoVis | HD-EPIC VQA challenge solution separating semantic and visual evidence | [Paper](https://arxiv.org/abs/2605.29402) |
 | HiERO-StepG | 2026-05 | CVPR 2026 EgoVis | Hierarchical activity-understanding solution for the Ego4D Step Grounding Challenge | [Paper](https://arxiv.org/abs/2605.31227) |
-| JFAA | 2026-05 | CVPR 2026 EgoVis | JEPA-based EPIC-KITCHENS-100 action-anticipation challenge submission | [Paper](https://arxiv.org/abs/2605.20904) |
-| MARS CASTLE | 2026-05 | CVPR 2026 EgoVis | Multimodal agentic reasoning with source selection for CASTLE challenge QA | [Paper](https://arxiv.org/abs/2605.18176) |
-| MM-Conv | 2026-05 | arXiv | Egocentric VR referential-communication benchmark with speech, motion, gaze, and 3D scenes | [Paper](https://arxiv.org/abs/2605.21796) |
-| Mamba Ego Action Recognition | 2026-05 | arXiv | Cross-modal egocentric action recognition using RGB and hand-skeleton streams with Mamba | [Paper](https://arxiv.org/abs/2605.24302) |
-| TAP-JEPA | 2026-05 | CVPR 2026 EgoVis | EPIC-KITCHENS-100 action-anticipation runner-up using frozen V-JEPA 2.1 features | [Paper](https://arxiv.org/abs/2606.00662) |
 | TempRet | 2026-05 | CVPR 2026 EgoVis | Temporal enhancement and reranking for EPIC-KITCHENS-100 multi-instance retrieval | [Paper](https://arxiv.org/abs/2605.24470) |
 | Trajectory-Conditioned Egocentric Prediction | 2026-05 | arXiv | Future ego-view prediction conditioned on camera trajectory to disambiguate action outcomes | [Paper](https://arxiv.org/abs/2605.20388) |
-| VISTA | 2026-05 | CVPR 2026 EgoVis | V-JEPA plus StillFast temporal anticipator for Ego4D STA at EgoVis 2026 | [Paper](https://arxiv.org/abs/2605.20901) |
-| WristCompass | 2026-05 | arXiv | Learns ego-camera orientation from hand/camera kinematic coupling in manipulation video | [Paper](https://arxiv.org/abs/2605.30671) |
-| Zero-Shot Ego Object ReID | 2026-05 | arXiv | SAM3-feature fusion for zero-shot object re-identification in egocentric kitchen videos | [Paper](https://arxiv.org/abs/2605.26383) |
 | Ego-InBetween | 2026-04 | arXiv | Generates object state transitions in ego-centric videos from action instructions | [Paper](https://arxiv.org/abs/2604.17749) |
-| IMPACT | 2026-04 | arXiv | Ego-exo RGB-D industrial assembly dataset with bimanual, state, and anomaly annotations | [Paper](https://arxiv.org/abs/2604.10409) |
-| Personal Point of View 3DGS | 2026-04 | arXiv | Evaluation of dynamic 3D Gaussian splatting for egocentric scene reconstruction | [Paper](https://arxiv.org/abs/2604.23803) |
 | Syn2Seq Exo-to-Ego | 2026-04 | arXiv | Sequential exo-to-ego video generation from synchronized third-person views and camera poses | [Paper](https://arxiv.org/abs/2604.13793) |
 | UniversalVTG | 2026-04 | arXiv | Lightweight cross-dataset foundation model for video temporal grounding | [Paper](https://arxiv.org/abs/2604.08522) |
 | V-Nutri | 2026-04 | arXiv | Dish-level nutrition estimation from egocentric cooking videos | [Paper](https://arxiv.org/abs/2604.11913) |
-| VGGT-Segmentor | 2026-04 | arXiv | Geometry-enhanced segmentation across egocentric and exocentric views | [Paper](https://arxiv.org/abs/2604.13596) |
-| EgoReasoner | 2026-03 | arXiv | Task-adaptive structured thinking for egocentric 4D spatial and object reasoning | [Paper](https://arxiv.org/abs/2603.06561) |
-| FEEL | 2026-03 | arXiv | Force-synchronized egocentric kitchen-manipulation dataset from instrumented gloves | [Paper](https://arxiv.org/abs/2603.15847) |
-| Gaze-Regularized VLMs | 2026-03 | arXiv | Gaze-conditioned VLM training for ego-centric behavior understanding | [Paper](https://arxiv.org/abs/2603.23190) |
-| PAWS | 2026-03 | arXiv | Articulation extraction from large-scale hand-object interactions in egocentric video | [Paper](https://arxiv.org/abs/2603.25539) |
-| Recurrent Reasoning VLM | 2026-03 | arXiv | Recurrent VLM reasoning for long-horizon embodied task-progress estimation | [Paper](https://arxiv.org/abs/2603.17312) |
-| Static Scene Reconstruction from Dynamic Egocentric Videos | 2026-03 | arXiv | Mask-aware 3D reconstruction pipeline for long-form dynamic egocentric video | [Paper](https://arxiv.org/abs/2603.22450) |
-| Ego4OOD | 2026-01 | arXiv | Covariate-shift benchmark for egocentric video domain generalization | [Paper](https://arxiv.org/abs/2601.17056) |
-| Event-VStream | 2026-01 | arXiv | Event-driven long-video stream understanding for real-time video-language systems | [Paper](https://arxiv.org/abs/2601.15655) |
-| HD-EPIC VQA T-CoT | 2026-01 | arXiv | HD-EPIC VQA solution with temporal chain-of-thought prompting and Qwen2.5-VL adaptation | [Paper](https://arxiv.org/abs/2601.10228) |
-| Robust Egocentric Visual Attention | 2026-01 | arXiv | Language-guided scene-context model for egocentric visual attention prediction | [Paper](https://arxiv.org/abs/2601.01818) |
-### Video-Language and Long-Video Models
-| Resource | Released | Venue | What it contributes | Link |
-| :--- | :---: | :---: | :--- | :---: |
-| Temporal Action Graphs for Ego VLMs | 2026-06 | arXiv | Converts egocentric videos into narratives and temporal action graphs for in-context action recognition with open-weight VLMs | [Paper](https://arxiv.org/abs/2606.15417) |
-| ReRe Cross-View Revisiting | 2026-06 | ICML 2026 | Training-free spatial reasoning that revisits egocentric conclusions through synthesized complementary novel-view videos | [Project](https://zhenjiemao.github.io/ReRe/) |
-| CASTLE KG Retrieval | 2026-06 | CVPR 2026 EgoVis | Agentic long-context video understanding with video knowledge graphs and hierarchical retrieval for the CASTLE challenge | [Paper](https://arxiv.org/abs/2606.01933) |
-| OSGNet + MLLM Reranking | 2026-05 | CVPR 2026 EgoVis | Champion Ego4D Episodic Memory Challenge solution for NLQ and GoalStep using MLLM reranking over OSGNet candidates | [GitHub](https://github.com/iLearn-Lab/CVPR25-OSGNet) |
-| OmniEgo-R2 | 2026-05 | CVPR 2026 EgoVis | Routed reasoning framework for EgoCross; second place in both Source-Limited and Open-Source tracks | [GitHub](https://github.com/Lee-zixu/OmniEgo-R2) |
-| Reflective Dialogue EgoCross | 2026-05 | CVPR 2026 EgoVis | Inference-time Teacher/Solver reflective dialogue for EgoCross support-set adaptation without fine-tuning | [Paper](https://arxiv.org/abs/2605.27885) |
-| EgoSim | 2026-04 | arXiv | Closed-loop egocentric world simulator with 3D grounding and dynamic state updates for multi-stage interaction generation | [Paper](https://arxiv.org/abs/2604.01001) |
-| EgoMotion | 2026-04 | arXiv | Hierarchical reasoning + diffusion for egocentric vision-language motion generation | [Paper](https://arxiv.org/abs/2604.19105) |
 | LOME | 2026-03 | arXiv | Action-conditioned egocentric world model generating photorealistic human-object interactions from image, text, and per-frame actions | [Paper](https://arxiv.org/abs/2603.27449) |
 | Temporal-Aware Ego VLM | 2026-03 | arXiv | Training scheme that incentivizes temporal awareness in egocentric video-understanding models | [Paper](https://arxiv.org/abs/2603.27184) |
 | EgoForge | 2026-03 | arXiv | Goal-directed egocentric world simulator that rolls out first-person video from a single image and a high-level instruction | [Paper](https://arxiv.org/abs/2603.20169) |
 | DreamDojo | 2026-02 | ICML 2026 | Generalist robot world model pretrained on 44K hours of egocentric human video with continuous latent actions, distilled to real time | [GitHub](https://github.com/NVIDIA/DreamDojo) |
 | Hand2World | 2026-02 | arXiv | Autoregressive egocentric world model generating first-person interaction video from free-space hand gestures | [Paper](https://arxiv.org/abs/2602.09600) |
 | Edge Episodic Memory QA | 2026-02 | arXiv | On-device dual-thread MLLM for real-time egocentric episodic-memory QA (QAEgo4D-Closed) on the edge | [Paper](https://arxiv.org/abs/2602.22455) |
 | EgoGraph | 2026-02 | arXiv | Training-free temporal knowledge graph for ultra-long egocentric video understanding | [Paper](https://arxiv.org/abs/2602.23709) |
 | EGAgent | 2026-01 | arXiv | Entity-scene-graph agent for very-long egocentric video understanding over all-day wearable streams | [GitHub](https://github.com/facebookresearch/egagent) |
 | Walk through Paintings | 2026-01 | arXiv | Egocentric world models from internet priors that generate first-person scene walkthroughs | [Paper](https://arxiv.org/abs/2601.15284) |
-| Ropedia Xperience-10M Task Baselines | 2026 | Hugging Face | Public Xperience-10M sample task definitions, baselines, metrics, and scale-up evaluation notes | [Hugging Face](https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines) |
-| EgoMAS | 2026 | project page | Shared-memory baseline for multi-agent egocentric video QA | [Project](https://ma-egoqa.github.io/) |
 | EgoControl | 2025-11 | arXiv | Pose-controllable egocentric video diffusion conditioned on sequences of 3D full-body poses | [Paper](https://arxiv.org/abs/2511.18173) |
 | DMC3 Ego VideoQA | 2025-10 | arXiv | Counterfactual contrastive construction for egocentric VideoQA across event descriptions and hand-object interaction cues | [Paper](https://arxiv.org/abs/2510.20285) |
 | EgoThinker | 2025-10 | NeurIPS 2025 | Egocentric reasoning model with spatio-temporal chain-of-thought and RL fine-tuning | [Paper](https://arxiv.org/abs/2510.23569) |
@@ -691,6 +649,7 @@ Fresh entries from the June 2026 source scan. Most are marked `watch` until code
 | HumanScale | 2026-06 | arXiv | Shows filtered, labeled egocentric human video can outperform teleoperated real-robot data for embodied pretraining | [Paper](https://arxiv.org/abs/2606.20521) |
 | Do as I Do | 2026-06 | arXiv | Retargets human hand-object interactions from in-the-wild monocular video into executable dexterous robot trajectories | [Paper](https://arxiv.org/abs/2606.19333) |
 | Motion-Focused Latent Action VLA | 2026-06 | IROS 2026 | Extracts motion-focused latent actions from unlabeled human EgoVideos for cross-embodiment VLA pretraining and adaptation | [Paper](https://arxiv.org/abs/2606.18955) |
 | EgoPhys | 2026-06 | arXiv | Learns deformable-object physics digital twins from egocentric RGB interaction video for robot planning | [Project](https://hjhyunjinkim.github.io/EgoPhys) |
 | EDITH | 2026-06 | arXiv | Streams first-person view, gaze, and speech from smart glasses into hierarchical robot policies for natural HRI | [Project](https://project-edith.github.io) |
 | Divide Deliberate Decide | 2026-06 | arXiv | Local zero-shot multi-agent VLM framework for fine-grained egocentric action recognition | [Paper](https://arxiv.org/abs/2606.17627) |
@@ -704,6 +663,14 @@ Fresh entries from the June 2026 source scan. Most are marked `watch` until code
 | ACE-Ego-0 | 2026-06 | arXiv | Unifies egocentric human video with robot/sim data via reliability-aware weighting for VLA pretraining (RoboCasa GR1, RoboTwin 2.0) | [Paper](https://arxiv.org/abs/2606.17200) |
 | EgoAction | 2026-05 | CVPR 2026 | CVPR 2026 EPIC-KITCHENS action detection challenge pipeline | [Paper](https://arxiv.org/abs/2605.24496) |
 | EgoAdapt | 2026-05 | CVPR 2026 | CVPR 2026 HD-EPIC VQA challenge inference-time adaptation pipeline | [Paper](https://arxiv.org/abs/2605.24500) |
 | StableHand | 2026-05 | arXiv | Quality-aware flow-matching baseline for world-space dual-hand motion estimation from egocentric video | [Project](https://huajian-zeng.github.io/projects/stablehand/) |
 | HumanEgo | 2026-05 | arXiv | Zero-shot robot learning from minutes of human egocentric videos via entity-level hand-object representations and flow-matching policies | [Project](https://humanego-ai.github.io) |
 | EgoForce Hand Pose | 2026-05 | SIGGRAPH 2026 | Monocular egocentric 3D hand pose and shape reconstruction across fisheye, perspective, and wide-FOV camera models | [Project](https://dfki-av.github.io/EgoForce) |
@@ -711,9 +678,13 @@ Fresh entries from the June 2026 source scan. Most are marked `watch` until code
 | EgoExo-WM | 2026-05 | arXiv | Converts exocentric video into egocentric world-model training data using body-pose priors | [Project](https://vision.cs.utexas.edu/projects/EgoExo-WM/) |
 | MotionGRPO | 2026-05 | ICML 2026 | GRPO-based post-training for full-body 3D motion recovery from head-mounted device signals | [Paper](https://arxiv.org/abs/2605.05680) |
 | ActiveGlasses | 2026-04 | arXiv | Learns robot manipulation from smart-glasses ego-centric human demonstrations and transfers active vision to a robot perception arm | [Paper](https://arxiv.org/abs/2604.08534) |
 | Gaze-SoM HOI Anticipation | 2026-04 | arXiv | Gaze and set-of-mark prompting in VLLMs for hand-object-interaction anticipation from egocentric video | [Paper](https://arxiv.org/abs/2604.03667) |
 | EgoFlow | 2026-04 | arXiv | Gradient-guided flow matching for physically plausible 6DoF object-motion generation from egocentric video | [Paper](https://arxiv.org/abs/2604.01421) |
 | UniDex | 2026-03 | arXiv | Robot foundation suite for universal dexterous hand control learned from egocentric human videos | [Paper](https://arxiv.org/abs/2603.22264) |
 | EgoHOI World Model | 2026-03 | arXiv | Physics-informed egocentric world model that synthesizes contact-consistent hand-object interactions from action signals alone | [Paper](https://arxiv.org/abs/2603.13615) |
 | STAformer++ Affordance-Aware Anticipation | 2026-02 | arXiv | Integrates temporal attention, scene affordance memory, and interaction hotspots for short-term object-interaction anticipation on Ego4D and EPIC-KITCHENS | [Paper](https://arxiv.org/abs/2602.14837) |
 | Neck-Mounted Gaze (GLC) | 2026-02 | arXiv | Transformer gaze estimator for a shoulder-level neck-mounted camera with out-of-bound classification and multi-view co-learning | [Paper](https://arxiv.org/abs/2602.11669) |
@@ -745,7 +716,7 @@ Fresh entries from the June 2026 source scan. Most are marked `watch` until code
 | Diff-IP2D | 2024-05 | arXiv | Non-autoregressive diffusion forecasting of 2D hand trajectories and object affordances with camera-egomotion conditioning | [GitHub](https://github.com/IRMVLab/Diff-IP2D) |
 | EffHandEgoNet | 2024-04 | FG 2024 | Egocentric 2D hand pose and action-recognition method for smart-glasses RGB input on H2O and FPHA | [Paper](https://arxiv.org/abs/2404.09308) |
 | EgoLifter | 2024-03 | ECCV 2024 | Open-world 3D segmentation decomposing natural egocentric video into individual 3D objects via 3D Gaussians and SAM | [GitHub](https://github.com/facebookresearch/egolifter) |
-| EgoPoseFormer | 2024 | GitHub | Transformer baseline for stereo egocentric 3D human pose estimation | [GitHub](https://github.com/ChenhongyiYang/egoposeformer) |
 | Get a Grip | 2023-12 | arXiv | Reconstructs stable hand-object grasps from egocentric video | [Project](https://zhifanzhu.github.io/getagrip) |
 | Aria-NeRF | 2023-11 | arXiv | Multimodal egocentric view synthesis for Project Aria-style capture | [Paper](https://arxiv.org/abs/2311.06455) |
 | Egocentric Whole-Body MoCap | 2023-11 | arXiv | FisheyeViT plus diffusion refinement for egocentric whole-body motion capture | [Paper](https://arxiv.org/abs/2311.16495) |
@@ -764,7 +735,7 @@ Fresh entries from the June 2026 source scan. Most are marked `watch` until code
 | TransFusion | 2023-01 | IEEE TCSVT 2024 | Summarizes past egocentric context in language to improve multimodal object-interaction anticipation | [Paper](https://arxiv.org/abs/2301.09209) |
 | Ego-Only | 2023-01 | arXiv | Egocentric action detection without exocentric pretraining transfer | [Paper](https://arxiv.org/abs/2301.01380) |
 | EgoSTARK | 2023-01 | arXiv | Adapted long-term tracker baseline for EgoTracks | [Paper](https://arxiv.org/abs/2301.03213) |
-| AV-CONV | 2023 | project page | Audio-visual conversational graph prediction from ego/exo conversation | [Project](https://vjwq.github.io/AV-CONV/) |
 | EgoLoc | 2022-12 | ICCV 2023 | Stronger Ego4D visual-query 3D object localization with camera-pose and localization baselines | [Paper](https://arxiv.org/abs/2212.06969) |
 | CONE | 2022-11 | ECCV 2022 Workshop | Coarse-to-fine alignment framework for Ego4D Natural Language Queries | [Paper](https://arxiv.org/abs/2211.08776) |
 | InternVideo-Ego4D | 2022-11 | Ego4D Workshop 2022 | Pack of champion Ego4D challenge solutions spanning episodic memory, forecasting, hand-object, and audio/social tracks | [Paper](https://arxiv.org/abs/2211.09529) |
@@ -774,7 +745,7 @@ Fresh entries from the June 2026 source scan. Most are marked `watch` until code
 | HOI Forecast / Interaction Hotspots | 2022-04 | CVPR 2022 | Forecasts future hand trajectories and interaction hotspots on next-active objects | [Project](https://stevenlsw.github.io/hoi-forecast) |
 | EgoGAN | 2022-03 | arXiv | Generative future hand-mask forecasting from egocentric video | [Paper](https://arxiv.org/abs/2203.11305) |
 | Untrimmed Action Anticipation | 2022-02 | arXiv | Reframes egocentric anticipation for untrimmed first-person streams | [Paper](https://arxiv.org/abs/2202.04132) |
-| EgoHOS model | 2022 | GitHub | Context-aware hand-object segmentation and augmentation pipeline | [GitHub](https://github.com/owenzlz/EgoHOS) |
 | E2(GO)MOTION | 2021-12 | CVPR 2022 | Motion-augmented event-stream representation for egocentric action recognition | [Paper](https://arxiv.org/abs/2112.03596) |
 | Temporal-Context Ego Action Recognition | 2021-11 | BMVC 2021 | Multimodal transformer that uses surrounding temporal context for egocentric action recognition | [Paper](https://arxiv.org/abs/2111.01024) |
 | Exo-to-Ego Video Synthesis | 2021-07 | ACM MM 2021 | Cross-view synthesis model that generates egocentric video from exocentric video | [Paper](https://arxiv.org/abs/2107.03120) |
@@ -805,28 +776,30 @@ Fresh entries from the June 2026 source scan. Most are marked `watch` until code
 | Future Person Localization | 2017-11 | CVPR 2018 | Predicts where people will appear in future frames from a first-person wearable camera | [Paper](https://arxiv.org/abs/1711.11217) |
 | First-Person Activity Forecasting | 2016-12 | ICCV 2017 Oral | DARKO online inverse-reinforcement-learning system for forecasting actions and active objects | [Paper](https://arxiv.org/abs/1612.07796) |
 | Egocentric FOV Localization | 2015-10 | WACV 2015 | Localizes the camera wearer's first-person field of view in overhead/surveillance video | [Paper](https://arxiv.org/abs/1510.02073) |
-| First-Person Pose Recognition | 2015 | CVPR 2015 | Uses egocentric workspaces and camera geometry for first-person human pose recognition | [Paper](https://openaccess.thecvf.com/content_cvpr_2015/html/Rogez_First-Person_Pose_Recognition_2015_CVPR_paper.html) |
 ### Practical Tooling
 | Tool | Released | Venue | Use |
 | :--- | :---: | :---: | :--- |
 | [EgoKit](https://egokit.chuange.org/) | 2026-05 | arXiv | Low-cost synchronized ego/wrist recording workflow across phones, smart glasses, and XR hosts. |
 | [MobileEgo Anywhere](https://arxiv.org/abs/2605.05945) | 2026-05 | arXiv | Commodity-phone infrastructure for hour-plus egocentric trajectories, STERA processing, and long-form VLA data collection. |
 | [VisionClaw](https://arxiv.org/abs/2604.03486) | 2026-04 | arXiv | Always-on wearable AI-agent system on Ray-Ban Meta smart glasses, coupling live egocentric perception with speech-driven task execution. |
-| [HOMIE-toolkit](https://github.com/Ropedia/HOMIE-toolkit) | 2026 | GitHub | Loading and visualizing Xperience-10M HDF5 annotations, calibration, SLAM, hand/body mocap, depth, IMU, and point clouds. |
 | [RaycastGrasp](https://arxiv.org/abs/2510.22113) | 2025-10 | ICIR 2025 | Egocentric gaze-guided MR headset interface for robotic object retrieval and manipulation. |
 | [AiGet](https://arxiv.org/abs/2501.16240) | 2025-01 | CHI 2025 | Smart-glasses assistant for gaze/context/profile-driven informal learning during everyday moments. |
 | [HUX](https://arxiv.org/abs/2407.19492) | 2024-07 | arXiv | Always-on smart-glasses / XR companion concept with gaze, environment context, verbal context, and memory storage. |
-| [HOT3D tooling](https://facebookresearch.github.io/hot3d/) | 2024 | project page | Loading HOT3D hand/object/camera pose annotations and models. |
 | [EgoBlur](https://arxiv.org/abs/2308.13093) | 2023-08 | arXiv | Privacy-preserving blur pipeline and responsible-innovation analysis for Project Aria egocentric capture. |
-| [Ego-Exo4D CLI and docs](https://ego-exo4d-data.org/) | 2023 | project page | Downloading synchronized ego-exo data and annotations. |
-| [EgoObjects API](https://github.com/facebookresearch/EgoObjects) | 2023 | GitHub | Working with category and instance-level egocentric object labels. |
-| [Project Aria Tools](https://github.com/facebookresearch/projectaria_tools) | 2022 | GitHub | Reading Aria VRS, calibration, MPS, trajectory, gaze, and dataset artifacts. |
-| [VISOR API](https://github.com/epic-kitchens/VISOR) | 2022 | GitHub | Loading dense EPIC-KITCHENS hand/object masks and relations. |
-| [HOI4D tooling](https://hoi4d.github.io/) | 2022 | project page | Loading RGB-D frames, point clouds, object meshes, and pose/segmentation annotations. |
 | [PAL](https://arxiv.org/abs/2105.10735) | 2021-05 | CVPR 2021 EPIC Workshop | Wearable personalized visual-context detection for privacy-preserving intelligence augmentation. |
-| [Ego4D CLI and docs](https://ego4d-data.org/) | 2021 | project page | Downloading and working with Ego4D data after license approval. |
 ## Adjacent and Related Resources
@@ -903,15 +876,9 @@ These entries are promising but should be rechecked before treating them as stab
 ## Contributing
-Contributions are welcome through pull requests and issues. See [`CONTRIBUTING.md`](CONTRIBUTING.md) for the inclusion policy, status rules, and style.
-The catalog is machine-checked. Before opening a pull request, run the validator:
-```bash
-ruby scripts/validate_catalog.rb
-```
-It verifies the catalog shape, statuses, kinds, README local links and assets, and that the resources badge and "Updated" date stay in sync with [`data/resources.yml`](data/resources.yml). The same check runs in CI on every push and pull request.
 ## Cite This Atlas

 </p>
 <p align="center">
+  <strong>A curated map of egocentric AI — datasets, benchmarks, models, and tools.</strong>
 </p>
 <!-- LANG-BAR:START -->
 | 456 egocentric resources | 125 datasets, 81 benchmarks, 226 models, and 23 toolkits, plus a Project Aria collection hub — across vision, robotics, memory, and AR. Four related non-egocentric resources are listed separately. |
 | 6 research lanes | Foundation video, procedure/action, hands and 3D, memory/reasoning, robotics/VLA, and AR/wearable sensing. |
 | 5 access states | `open`, `request`, `benchmark`, `partial`, and `watch` keep availability visible before you plan experiments. |
+| Machine-checked catalog | [`data/resources.yml`](data/resources.yml) is the source for type, year, status, URL, tasks, and provenance — and CI keeps the public artifacts in sync. |
 | Reader-first tables | Each entry is short enough to scan, then links out to the official page, paper, code, or dataset portal. |
 <p align="center">
 ## Milestones
+The landmark works that shaped egocentric AI — a fast on-ramp from the field's origins to its current frontier.
 <p align="center">
   <img src="assets/awesome-egocentric-milestones.png" alt="Illustrated milestone timeline for representative egocentric AI works" width="100%">
 <!-- MILESTONES:START (generated by build_artifacts.rb — do not edit by hand) -->
 <!-- MILESTONES:END -->
 ## Start Here
 | [EgoCS-400K](https://arxiv.org/abs/2606.18180) | 2026-06 | arXiv | 400K+ first-person Counter-Strike gameplay videos (10K hours, 13 maps) with aligned actions, player state, camera motion, and game events | Action-conditioned interactive world models from first-person gameplay | watch |
 | [Ego-1K](https://huggingface.co/datasets/facebook/ego-1k) | 2026-03 | CVPR 2026 | Nearly 1,000 synchronized multiview egocentric videos from a custom 12-camera plus VR-headset rig | Dynamic 3D/4D scene understanding and novel view synthesis from ego rigs | open |
 | [EgoCrowds / CrowdEraser](https://arxiv.org/abs/2603.29036) | 2026-03 | arXiv | Semi-synthetic paired crowded/empty clips from real egocentric walking-tour video; CrowdEraser diffusion removes crowds for humanless walkthroughs | First-person walking-tour video editing and environment modeling | watch |
+| [Xperience-10M](https://huggingface.co/datasets/ropedia-ai/xperience-10m) | 2026-03 | Hugging Face | Ropedia release on Hugging Face; 10M experiences, 10K hours, six video streams, audio, stereo depth, camera pose, hand/body mocap, IMU, hierarchical language, ~1 PB total | Embodied AI, world models, robot learning from human experience, sensor fusion, 3D/4D understanding | request |
+| [Xperience-10M Sample](https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample) | 2026-03 | Hugging Face | Public sample episode for Xperience-10M with six rows on Hugging Face and `cc-by-nc-4.0` terms | Loader testing, demos, task-suite prototyping, annotation inspection | open |
 | [Look and Tell](https://arxiv.org/abs/2510.22672) | 2025-10 | NeurIPS 2025 Workshop | 25 participants, Project Aria plus stationary cameras, gaze/speech/video, 3D reconstructions | Referential communication across ego and exo viewpoints | watch |
 | [EgoBlind](https://arxiv.org/abs/2503.08221) | 2025-03 | NeurIPS 2025 D&B | 1,392 first-person videos from blind and visually impaired users, 5,311 questions posed or verified by blind users | Assistive egocentric VideoQA for blind users | watch |
 | [HD-EPIC](https://arxiv.org/abs/2502.04144) | 2025-02 | CVPR 2025 | 41 hours, 9 kitchens, dense fine-grained labels, 3D fixture annotations, audio events, VQA | Detailed kitchen understanding, VQA, 3D-aware ego reasoning, audio-event recognition | open |
 | [Ego4D](https://ego4d-data.org/) | 2021-10 | CVPR 2022 | 3,670+ hours from 900+ camera wearers, multiple countries, video/audio/gaze/stereo/3D/narrations depending on subset | Long-form ego video, episodic memory, social, hand-object, forecasting, audio-visual tasks | request |
 | [EasyCom](https://arxiv.org/abs/2107.04174) | 2021-07 | arXiv | AR glasses egocentric multi-channel audio and wide-FOV RGB for noisy conversations | Speech enhancement, source localization, conversation assistance | open |
 | [EPIC-KITCHENS-100](https://epic-kitchens.github.io/) | 2020-06 | IJCV 2022 | 100 hours, 20M frames, 90K actions, 45 kitchens, narrations and dense action labels | Kitchen action recognition, anticipation, action detection, retrieval | open |
+| [EgoCom / Ego audio-visual correspondence](http://vision.cs.utexas.edu/projects/ego_av_corr/) | 2023-07 | arXiv | Egocentric video with spatial audio for conversation and audio-visual correspondence tasks | Active speaker detection, spatial audio denoising, conversational graph reasoning | open |
 | [From Third Person to First Person](https://arxiv.org/abs/1812.00104) | 2018-12 | CVPR 2019 | Ego/exo video synthesis and retrieval datasets plus baselines for bridging third-person social video to first-person views | Cross-view synthesis, retrieval, and ego-exo transfer | watch |
 | [EPIC-KITCHENS](https://epic-kitchens.github.io/) | 2018-04 | ECCV 2018 | 55 hours / 11.5M frames from 32 kitchens with narrations, 39.6K action segments, and object boxes | Original large-scale kitchen action recognition and anticipation benchmark | open |
 | [MV-UMI](https://arxiv.org/abs/2509.18757) | 2025-09 | arXiv | Multi-view UMI adds third-person context to wrist egocentric observations | Broad-scene context for cross-embodiment manipulation | watch |
 | [InterVLA](https://arxiv.org/abs/2508.04681) | 2025-08 | ICCV 2025 | 11.4 hours and 1.2M frames with 2 ego and 5 exo views, human/object motions, and verbal commands | Vision-language-action and motion estimation | watch |
 | [EgoDex](https://arxiv.org/abs/2505.11709) | 2025-05 | ICLR 2026 | 829 hours of Apple Vision Pro egocentric video, 194 tabletop tasks, 3D hand/finger tracking | Dexterous manipulation, imitation learning, human-to-robot hand trajectory prediction | watch |
+| [EgoVLA](https://rchalyang.github.io/EgoVLA/) | 2025-07 | project page | VLA training from egocentric human videos plus robot fine-tuning | Human-video-to-robot policy transfer | watch |
 | [FastUMI](https://arxiv.org/abs/2409.19499) | 2024-09 | arXiv | UMI redesign reporting 10K+ real-world trajectories across 22 everyday tasks | Faster hardware-independent UMI-style collection | watch |
 | [EgoPAT3D / EgoPAT3Dv2](https://arxiv.org/abs/2403.05046) | 2024-03 | ICRA 2024 | 1M+ RGB-D/IMU frames in the original task; v2 expands egocentric 3D action-target prediction | Human-robot interaction, 3D target anticipation, manipulation safety | open |
 | [Universal Manipulation Interface / UMI](https://umi-gripper.github.io/) | 2024-02 | RSS 2024 | Hand-held GoPro gripper interface and policy stack for in-the-wild robot teaching | Wrist-view robot-free demonstrations and cross-embodiment policy transfer | open |
 | [EgoFun3D](https://arxiv.org/abs/2604.11038) | 2026-04 | arXiv | 271 egocentric videos with 3D geometry, part segmentation, articulation and function-template annotations | Interactive 3D object modeling from ego video | watch |
 | [SHOW3D](https://arxiv.org/abs/2603.28760) | 2026-03 | CVPR 2026 | In-the-wild 3D hand-object interactions from a back-mounted multi-camera rig synced to a worn VR headset, with multi-view 3D shape/pose and text | In-the-wild 3D hand-object reconstruction | watch |
 | [EgoXtreme](https://arxiv.org/abs/2603.25135) | 2026-03 | CVPR 2026 | Smart-glasses egocentric 6D object-pose dataset across industrial, sports, and rescue scenes with extreme motion blur, dynamic lighting, and occlusion | Robust 6D object pose under extreme egocentric conditions | watch |
+| [FEEL](https://arxiv.org/abs/2603.15847) | 2026-03 | arXiv | About 3M force-synchronized egocentric frames from kitchen manipulation with custom piezoresistive gloves | Contact-rich physical action and hand-object understanding | watch |
 | [Eva-3M / EvaPose](https://arxiv.org/abs/2602.23618) | 2026-02 | CVPR 2026 | 3.0M+ egocentric HPE frames, including 435K keypoint-visibility labels, plus visibility-aware pose estimation | Visibility-aware egocentric human pose | watch |
 | [WristPP](https://zhenqis123.github.io/WristPP/) | 2026-02 | CHI 2026 submission | Wrist-worn wide-FOV RGB system with a 133K-frame pose-pressure dataset from 20 subjects | Mobile hand pose and pressure interaction | watch |
 | [OpenTouch](https://opentouch-tactile.github.io/) | 2025-12 | arXiv | 5.1 hours of synchronized egocentric video-touch-pose data, 2,900 curated clips, text annotations, and retrieval/classification benchmarks | Full-hand tactile grounding for real-world interaction | watch |
 | [EgoPressure](https://yiming-zhao.github.io/EgoPressure/) | 2024-09 | CVPR 2025 | 5.0 hours, 21 participants, a moving egocentric camera plus 7 stationary RGB-D cameras, hand pose meshes, and fine-grained per-contact touch pressure (CVPR 2025) | Hand pressure and pose from egocentric vision | open |
 | [HOT3D](https://facebookresearch.github.io/hot3d/) | 2024-06 | CVPR 2025 | 833+ minutes, 3.7M+ images, Aria and Quest 3, gaze, point clouds, 3D hand/object/camera poses | 3D hand-object tracking for AR/VR manipulation | open |
 | [UnrealEgo2 / UnrealEgo-RW](https://arxiv.org/abs/2401.00889) | 2024-01 | arXiv | Expanded stereo egocentric pose datasets from synthetic and real-world capture | Stereo egocentric human pose estimation | watch |
+| [EgoEVHands](https://github.com/ZJUWang01/EgoEV-HandPose) | 2026-05 | arXiv | Stereo event-camera egocentric hand dataset with 5,419 sequences and 3D/2D keypoints | Event-based bimanual hand pose and gesture recognition | watch |
 | [POV-Surgery](https://batfacewayne.github.io/POV_Surgery_io/) | 2023-07 | MICCAI 2023 | Egocentric surgical video for hand and tool pose estimation during operating-room activities | Surgical hand-tool pose and interaction understanding | open |
 | [EgoHumans](https://arxiv.org/abs/2305.16487) | 2023-05 | ICCV 2023 | 125K+ egocentric images for in-the-wild multi-human tracking, 2D/3D pose, and mesh recovery | Egocentric multi-human perception and tracking | partial |
 | [EgoTracks](https://arxiv.org/abs/2301.03213) | 2023-01 | NeurIPS 2023 | Long-term egocentric visual object tracking annotations from Ego4D | Tracking, re-detection, embodied object persistence | open |
 | [ARCTIC](https://arctic.is.tue.mpg.de/) | 2022-04 | CVPR 2023 | 2.1M frames with bimanual hand/object meshes, articulated objects, and contact | Dexterous bimanual manipulation, contact, reconstruction | request |
 | [GIMO](https://github.com/y-zheng18/GIMO) | 2022-04 | ECCV 2022 | Ego-centric views, gaze, scene scans, and high-quality body pose sequences | Gaze-informed human motion prediction in scene context | open |
 | [HOI4D](https://hoi4d.github.io/) | 2022-03 | CVPR 2022 | 2.4M RGB-D egocentric frames, 4,000+ sequences, 800 objects, 16 categories, 610 rooms | 4D human-object interaction, segmentation, object pose tracking, action segmentation | open |
+| [EgoBody](https://sanweiliti.github.io/egobody/egobody.html) | 2021-12 | ECCV 2022 | HoloLens2 egocentric RGB/depth/eye/head/hand data with 3D body pose and shape | Egocentric human pose, shape, motion, social interaction | open |
 | [H2O: Two Hands and Objects](https://arxiv.org/abs/2104.11181) | 2021-04 | ICCV 2021 | Synchronized multi-view RGB-D, two-hand 3D pose, 6D object pose, object meshes, camera poses | First-person two-hand interaction recognition and pose-aware HOI | open |
+| [TREK-150](https://machinelearning.uniud.it/datasets/trek150/) | 2021-08 | project page | 150 egocentric tracking sequences derived from EPIC-KITCHENS | Single-object tracking in egocentric video | open |
 | [Ego2Hands](https://arxiv.org/abs/2011.07252) | 2020-11 | arXiv | Large-scale composited RGB egocentric two-hand segmentation/detection | Robust unconstrained hand segmentation | open |
+| [xR-EgoPose](https://github.com/facebookresearch/xR-EgoPose) | 2019-07 | GitHub | Synthetic egocentric body pose data for XR headset viewpoints | 3D human pose from head-mounted cameras | open |
 | [EgoYouTubeHands / HandOverFace](https://arxiv.org/abs/1803.03317) | 2018-03 | CVPR 2018 | In-the-wild egocentric hand-segmentation study built around EgoYouTubeHands, HandOverFace, EGTEA, and EgoHands-style supervision | Hand detection and segmentation beyond lab capture | watch |
+| [FPHA / First-Person Hand Action](https://guiggh.github.io/publications/first-person-hands/) | 2017-04 | CVPR 2018 | 100K+ RGB-D frames, 45 action classes, 26 objects, 3D hand/object poses | 3D hand pose and first-person hand action recognition | open |
 | [EgoGesture](https://ieeexplore.ieee.org/document/8299578/) | 2017 | IEEE TMM 2018 | 24K+ gesture samples, 3M RGB-D frames, 50 subjects, 83 static and dynamic gestures across six scenes | First-person gesture recognition for wearable interaction | open |
+| [EgoHands](http://vision.soic.indiana.edu/projects/egohands/) | 2015-12 | ICCV 2015 | 48 Google Glass videos, 4,800 annotated hand images | Hand detection and segmentation | open |
+| [BEOID](https://dimadamen.github.io/BEOID/) | 2014-09 | BMVC 2014 | Gaze-tracked egocentric video of 8 users interacting with objects across 6 everyday locations (kitchen, workspace, printer, corridor, gym) | Discovering task-relevant objects and interaction modes | open |
 ### Daily Life, Memory, Assistance, and QA
 | [Wearable Product Localization](https://arxiv.org/abs/2601.12486) | 2026-01 | arXiv | Wearable assistive shopping system combining detection, VLM guidance, spatialized sonification, and corrective feedback | Product search and navigation for blind or low-vision users | watch |
 | [Egocentric Clinical Intent](https://arxiv.org/abs/2601.06750) | 2026-01 | arXiv | Benchmark for egocentric clinical intent understanding by medical multimodal LLMs over first-person clinical procedure video | Clinical intent reasoning for medical assistants | watch |
 | [WearVox](https://arxiv.org/abs/2601.02391) | 2026-01 | arXiv | Egocentric multichannel voice-assistant benchmark for spoken interaction grounded in first-person audio-visual context | Wearable voice-assistant evaluation | watch |
+| [EgoMemory](https://openreview.net/forum?id=T0em4hJCQb) | 2025-09 | OpenReview | 165,795 user-specific object annotations over 245 videos from 45 participants | Memory-augmented personalized retrieval | watch |
 | [Ego-EXTRA](https://fpv-iplab.github.io/Ego-EXTRA/) | 2025-12 | arXiv | 50 hours of expert-trainee procedural assistance dialogue, 15K+ VQA pairs | Egocentric video-language assistants and expert feedback | open |
 | [WearVQA](https://arxiv.org/abs/2511.22154) | 2025-11 | NeurIPS 2025 | 2,520 image-question-answer triplets across 7 domains and 10 task types under occluded/low-light/blurry wearable capture (NeurIPS 2025) | Smart-glasses VQA under real wearable conditions | watch |
 | [TeleEgo](https://arxiv.org/abs/2510.23981) | 2025-10 | arXiv | Streaming omni-modal benchmark with 3,291 QA items across memory, understanding, and cross-memory reasoning | Real-time egocentric AI assistant evaluation | watch |
 | [EgoLife](https://arxiv.org/abs/2503.03803) | 2025-03 | CVPR 2025 | 300 hours from six participants over one week with Meta Aria, third-person references, and EgoLifeQA | Long-term daily-life memory, personalized assistants, ultra-long QA | partial |
 | [EgoToM](https://arxiv.org/abs/2503.22152) | 2025-03 | arXiv | Theory-of-mind QA over Ego4D-style egocentric videos | Goals, beliefs, and next-action reasoning for camera wearers | watch |
 | [X-LeBench](https://arxiv.org/abs/2501.06835) | 2025-01 | arXiv | 432 simulated life logs over Ego4D-like footage, spanning 23 minutes to 16.4 hours | Extremely long egocentric video understanding | watch |
+| [EgoSelf](https://abie-e.github.io/egoself_project/) | 2026-04 | arXiv | Personalized egocentric assistant framework with graph memory | Personalization from long-term egocentric interaction memory | watch |
+| [EgoCross](https://github.com/MyUniverse0726/EgoCross) | 2025-08 | arXiv | About 1,000 QA pairs over surgery, industry, extreme sports, and animal-perspective clips | Cross-domain egocentric QA generalization | watch |
+| [EgoTextVQA](https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_EgoTextVQA_Towards_Egocentric_Scene-Text_Aware_Video_Question_Answering_CVPR_2025_paper.pdf) | 2025-06 | CVPR 2025 | Egocentric scene-text-aware video QA across housekeeping and driving scenes (CVPR 2025) | Reading and reasoning over first-person scene text | open |
 | [VidEgoThink](https://arxiv.org/abs/2410.11623) | 2024-10 | ICLR 2025 | Ego4D-based benchmark for video QA, hierarchy planning, visual grounding, and reward modeling | Embodied egocentric video understanding | benchmark |
 | [EgoThink](https://arxiv.org/abs/2311.15596) | 2023-11 | CVPR 2024 | First-person VQA benchmark covering six capability groups and twelve dimensions | First-person perspective reasoning for VLMs | benchmark |
 | [EgoSchema](http://egoschema.github.io/) | 2023-08 | NeurIPS 2023 | 5K+ curated multiple-choice QA pairs over 250+ hours from Ego4D clips | Very long-form video-language understanding | benchmark |
 | [EgoTaskQA](https://arxiv.org/abs/2210.03929) | 2022-10 | NeurIPS 2022 | Diagnostic QA benchmark for task dependencies, effects, intents, beliefs, and counterfactuals in ego video | Task-step reasoning and procedural QA | benchmark |
 | [AssistQ](https://showlab.github.io/assistq/) | 2022-03 | ECCV 2022 | 531 question-answer samples from 100 newly filmed instructional videos | Assistance-oriented video QA and affordance-centric task completion | open |
 | [MMAC Captions](https://arxiv.org/abs/2109.02955) | 2021-09 | ACM MM 2021 | Sensor-augmented egocentric-video captioning data around CMU-MMAC-style multimodal activity streams | Video captioning with RGB, audio, IMU, and text | watch |
+| [EgoVQA](https://openaccess.thecvf.com/content_ICCVW_2019/html/EPIC/Fan_EgoVQA_-_An_Egocentric_Video_Question_Answering_Benchmark_Dataset_ICCVW_2019_paper.html) | 2019-10 | ICCV 2019 | 600+ QA pairs over egocentric videos; an early first-person VideoQA benchmark | Classic egocentric VideoQA | open |
 | [First-Person Stories](https://arxiv.org/abs/1707.07863) | 2017-07 | ICIAP 2017 Workshop | 45K+ egocentric photo-stream images labeled for lifestyle patterns such as eating, socializing, and sedentary behavior | Lifelogging and daily-life behavior analysis | watch |
 ### Action, Procedure, Lifelogging, and Classic FPV
 | Resource | Released | Venue | Scale / signal | Best for | Status |
 | :--- | :---: | :---: | :--- | :--- | :---: |
 | [EgoMAGIC](https://arxiv.org/abs/2604.22036) | 2026-04 | arXiv | 3,355 egocentric field-medicine videos over 50 tasks from a head-mounted stereo camera with audio; 1.95M labels, 124 objects, action-detection challenge (Zenodo) | Field-medicine perception, action and object detection | open |
+| [IMPACT](https://arxiv.org/abs/2604.10409) | 2026-04 | arXiv | Ego-exo RGB-D industrial assembly dataset with bimanual, state, and anomaly annotations | Industrial assembly, procedural state tracking, anomaly recovery | watch |
 | [ENIGMA-360](https://arxiv.org/abs/2603.09741) | 2026-03 | arXiv | Ego-exo dataset for human behavior understanding in industrial scenarios with 360-degree and egocentric capture | Industrial ego-exo behavior understanding | watch |
 | [PEDESTRIAN](https://arxiv.org/abs/2512.19190) | 2025-12 | arXiv | 340 first-person pavement videos covering 29 urban sidewalk obstacle types, with deep-learning detection baselines | Pedestrian-safety obstacle detection from first-person video | watch |
 | [IndEgo](https://huggingface.co/datasets/FraunhoferIPK/IndEgo) | 2025-11 | NeurIPS 2025 | ~197h egocentric (plus ~97h exocentric) industrial collaborative work over assembly, logistics, inspection, and repair; gaze, narration, sound, motion, hand pose, point clouds | Industrial egocentric assistants and procedure understanding | open |
 | [EgoMe](https://arxiv.org/abs/2501.19061) | 2025-01 | arXiv | Real-world following-me dataset pairing exocentric demonstrations with egocentric imitation across everyday tasks | Egocentric imitation and cross-view following | watch |
 | [EgoExo-Fitness](https://github.com/iSEE-Laboratory/EgoExo-Fitness) | 2024-06 | ECCV 2024 | Synchronized ego and exo fitness videos with keypoint verification, execution comments, and quality scores (ECCV 2024) | Ego-exo full-body action quality and skill assessment | open |
 | [EgoExoLearn](https://github.com/OpenGVLab/EgoExoLearn) | 2024-03 | CVPR 2024 | 120 hours of egocentric plus demonstration videos with gaze and annotations | Bridging asynchronous ego and exo procedural activity | open |
+| [EgoSurgery](https://github.com/Fujiry0/EgoSurgery) | 2025-03 | arXiv | EgoSurgery-Phase (surgical phase recognition) and EgoSurgery-HTS (pixel-wise hand-tool segmentation of 14 tools) from egocentric open-surgery video (MICCAI 2024) | Surgical phase, hand, and tool understanding | open |
 | [CaptainCook4D](https://arxiv.org/abs/2312.14556) | 2023-12 | NeurIPS 2024 D&B | Procedural-activity dataset for understanding execution errors, later reused in multimodal temporal-action-segmentation work | Procedural errors and mistake-aware activity understanding | watch |
 | [IndustReal](https://arxiv.org/abs/2310.17323) | 2023-10 | WACV 2024 | Industrial-like egocentric procedure-step recognition dataset with execution errors | Industrial procedure recognition and error handling | watch |
 | [EGOFALLS](https://arxiv.org/abs/2309.04579) | 2023-09 | arXiv | Visual-audio egocentric dataset and benchmark for fall detection | Wearable fall detection and safety monitoring | watch |
 | [WEAR](https://mariusbock.github.io/wear/) | 2023-04 | IMWUT 2024 | 22 participants, 18 outdoor workouts, synchronized egocentric video and 3D acceleration across 11 locations (IMWUT 2024) | Vision-plus-inertial outdoor activity recognition | open |
+| [Visual Experience Dataset / VEDB](http://tamaraberg.com/visualexperience/) | 2024-02 | arXiv | 240+ hours egocentric video with gaze/head tracking in classic literature | Lifelogging, attention modeling, visual experience statistics | partial |
 | [EgoProceL](https://sid2697.github.io/egoprocel/) | 2022-07 | ECCV 2022 | 62 hours, 130 subjects, 16 procedural tasks | Key-step localization and procedure learning from ego videos | open |
+| [FT-HID](https://github.com/ENDLICHERE/FT-HID) | 2022-09 | GitHub | 90K+ RGB-D first- and third-person human interaction samples from 109 subjects | FPV/TPV aligned human interaction analysis | open |
 | [KrishnaCam / OAK](https://oakdata.github.io/) | 2021-08 | ICCV 2021 | Long-running Google Glass daily-life stream; OAK adds 17.5 hours of object annotations | Continual object detection, summarization, personal visual memory | partial |
 | [Home Action Genome / HOMAGE](https://homeactiongenome.org/) | 2021-05 | CVPR 2021 | 27 participants, synchronized ego and third-person views, 12 sensor types, hierarchical activity/action labels | Compositional multi-view home activity understanding | open |
 | [MECCANO](https://iplab.dmi.unict.it/MECCANO/) | 2020-10 | WACV 2021 | Industrial-like motorbike model assembly with RGB/depth/gaze variants | EHOI, active object detection, anticipation, industrial procedures | open |
 | [Ego-Engagement](https://arxiv.org/abs/1604.00906) | 2016-04 | arXiv | Egocentric video dataset and model for whether the wearer is engaged with people or objects | Engagement, attention, and object/person interaction cues | watch |
 | [Wrist-mounted ADL](https://arxiv.org/abs/1511.06783) | 2015-11 | CVPR 2016 | Synchronized head and wrist wearable-camera daily activities | Comparing head- versus wrist-mounted first-person views | open |
 | [Ego-Object Discovery / EDUB](https://arxiv.org/abs/1504.01639) | 2015-04 | arXiv | 4,912 egocentric daily-life photo-stream images from four users for unsupervised object discovery | Lifelog object discovery and detection | open |
+| [HUJI EgoSeg](https://www.vision.huji.ac.il/egoseg/) | 2014-06 | project page | Long egocentric videos for temporal segmentation | Egocentric event segmentation | partial |
 | [DogCentric](https://robotics.ait.kyushu-u.ac.jp/dog-centric-activity-dataset/) | 2014 | project page | Dog-mounted first-person activity videos | Animal egocentric activity recognition | open |
 | [JPL First-Person Interaction](https://ieeexplore.ieee.org/document/6909626) | 2013 | IEEE | First-person videos of people interacting with a humanoid observer | Human interaction recognition from first person | partial |
+| [First-Person Social Interactions](http://ai.stanford.edu/~alireza/publication/CVPR12.pdf) | 2012-06 | CVPR 2012 | Day-long head-mounted video of 8 subjects at a theme park, annotated for social interactions, roles, attention, and turn-taking | First egocentric social-interaction dataset | open |
+| [ADL Dataset](https://www.csc.kth.se/cvap/actions/) | 2012-06 | project page | Unscripted daily activity recordings with activity/object/hand annotations in classic literature | Daily living action recognition and object interaction | partial |
+| [UT Ego](http://vision.cs.utexas.edu/projects/egocentric/) | 2012-06 | project page | Long daily egocentric videos in classic summarization work | Temporal segmentation and summarization | partial |
+| [CMU-MMAC](http://kitchen.cs.cmu.edu/) | 2009-06 | CMU tech report 2009 | Multimodal kitchen-activity database: head-mounted egocentric video plus body IMUs, motion capture, and audio for 43 subjects cooking 5 recipes | One of the first egocentric activity datasets | open |
 ### Project Aria, AR/VR, and 3D Scene Resources
 | [Aria Everyday Activities / AEA](https://www.projectaria.com/datasets/aea/) | 2024-02 | arXiv | 143 daily activity sequences across five indoor locations with trajectories, point clouds, gaze, speech | Everyday AR perception, scene reconstruction, prompted segmentation | open |
 | [SANPO](https://arxiv.org/abs/2309.12172) | 2023-09 | WACV 2025 | Scene understanding, accessibility, and human navigation data for egocentric navigation and spatial assistance | Navigation and assistive scene understanding | watch |
 | [Aria Digital Twin / ADT](https://www.projectaria.com/datasets/adt/) | 2023-06 | arXiv | 200 real-world activity sequences, raw Aria streams, 6DoF poses, object poses, depth, segmentation, synthetic renderings | Egocentric 3D machine perception and digital-twin evaluation | open |
+| [Project Aria Datasets](https://www.projectaria.com/datasets/) | 2023-08 | arXiv | Official portal for Aria-based datasets and tooling | AR glasses, wearable sensing, scene reconstruction, gaze, SLAM | open |
 ## Benchmarks and Derived Annotations
 | [VL-MemKnG / WalkieKnowledgeT+](https://arxiv.org/abs/2606.17183) | 2026-06 | arXiv | Long egocentric navigation trajectories | Temporally distributed spatial-memory QA with hybrid graph and segment retrieval | watch |
 | [Plan, Watch, Recover / EgoProactive](https://arxiv.org/abs/2606.04970) | 2026-06 | arXiv | EgoProactive plus Pro2Bench over five established benchmarks | Proactive procedural assistance, out-of-plan detection, and recovery guidance | watch |
 | [EgoBench](https://arxiv.org/abs/2605.27820) | 2026-05 | arXiv | Egocentric video tasks | Interactive multimodal tool-using agents | watch |
+| [Beyond Motion Primitives](https://arxiv.org/abs/2605.27464) | 2026-05 | arXiv | Ego4D | Head-mounted IMU benchmark for behavioral activity recognition on smart glasses | watch |
 | [Ego-METAS](https://maria-sanvil.github.io/Ego-METAS-website/) | 2026-05 | arXiv | EgoExo4D, CMU-MMAC, CaptainCook4D | Online multimodal, energy-aware temporal action segmentation across RGB, audio, gaze, IMU, and monochrome streams | watch |
 | [EgoProx](https://arxiv.org/abs/2605.24456) | 2026-05 | CVPR 2026 | Egocentric 3D proximity QA | Intention, exploration, exploitation, and chain-of-actions spatial reasoning for MLLMs | watch |
 | [BARISTA](https://arxiv.org/abs/2605.12074) | 2026-05 | arXiv | 185 coffee-preparation videos | Scene graphs, masks, tracks, boxes, hand-object interactions, activities, and process-step reasoning | watch |
 | [TAVIS](https://arxiv.org/abs/2605.07943) | 2026-05 | arXiv | IsaacLab active-vision imitation tasks | Headcam vs fixed-cam evaluation, wrist/head active vision, and anticipatory-gaze metric | watch |
+| [MM-Conv](https://arxiv.org/abs/2605.21796) | 2026-05 | arXiv | 6.7 hours of egocentric VR interaction | Referential communication with speech, motion, gaze, and 3D scenes | watch |
 | [EgoExoMem](https://arxiv.org/abs/2605.18734) | 2026-05 | arXiv | Synchronized ego-exo videos | Cross-view memory QA | watch |
 | [EgoMemReason](https://arxiv.org/abs/2605.09874) | 2026-05 | arXiv | Week-long egocentric video | Entity, event, and behavior memory reasoning | watch |
 | [Ego2World](https://arxiv.org/abs/2605.13335) | 2026-05 | arXiv | HD-EPIC | Executable symbolic worlds from egocentric cooking video for belief-state planning | watch |
 | [MA-EgoQA](https://ma-egoqa.github.io/) | 2026-03 | arXiv | Multi-agent egocentric streams | Social, task coordination, theory-of-mind, temporal, environment QA | open |
 | [EgoAVU](https://github.com/facebookresearch/EgoAVU) | 2026-02 | CVPR 2026 | Egocentric audio-visual narrations | EgoAVU-Instruct (3M QAs) and EgoAVU-Bench (3K QAs) for audio-visual understanding (CVPR 2026 highlight) | open |
 | [SAW-Bench](https://arxiv.org/abs/2602.16682) | 2026-02 | arXiv | Ray-Ban Meta smart-glasses video | Observer-centric situated awareness and physically grounded spatial reasoning | watch |
+| [Ego4OOD](https://arxiv.org/abs/2601.17056) | 2026-01 | arXiv | Egocentric video action recognition | Covariate-shift benchmark for egocentric domain generalization | watch |
 | [Sanpo-D](https://arxiv.org/abs/2601.18100) | 2026-01 | arXiv | Sanpo egocentric navigation video | Spatial-conditioned reasoning over long first-person videos with fine-grained spatial re-annotation | watch |
+| [Ropedia Xperience-10M Task Suite](https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite) | 2026-03 | Hugging Face | Xperience-10M Sample | 12 embodied-AI task contracts, sample baselines, and evaluation protocol | open |
 | [EgoExo-Con](https://arxiv.org/abs/2510.26113) | 2025-10 | arXiv | Synchronized ego-exo videos | View-invariant temporal verification and grounding; introduces View-GRPO | watch |
 | [LaMAria](https://lamaria.ethz.ch) | 2025-09 | ICCV 2025 | Glasses-like wearable capture | City-scale egocentric visual-inertial SLAM with centimeter ground truth | open |
 | [EgoIllusion](https://arxiv.org/abs/2508.12687) | 2025-08 | arXiv | Egocentric video | Hallucination benchmark probing fabricated objects, actions, and sounds in multimodal models | watch |
 | [EgoExoBench](https://arxiv.org/abs/2507.18342) | 2025-07 | arXiv | Public ego-exo video datasets | 7,300+ QA pairs over semantic alignment, viewpoint association, and temporal reasoning | watch |
 | [EASG-Bench](https://github.com/fpv-iplab/EASG-bench) | 2025-06 | ICCV 2025 Workshop | Egocentric action scene graphs | Relation and temporal QA | open |
 | [HD-EPIC VQA Challenge](https://arxiv.org/abs/2502.04144) | 2025-02 | CVPR 2025 | HD-EPIC | Recipe, ingredient, nutrition, fine-grained action, 3D perception, object motion, gaze | benchmark |
+| [EgoCross](https://github.com/MyUniverse0726/EgoCross) | 2025-08 | arXiv | Cross-domain ego clips | Surgery, industry, extreme sports, animal-perspective QA | watch |
 | [EgoHOIBench / EgoNCE++](https://github.com/xuboshen/EgoNCEpp) | 2024-05 | ICLR 2025 | Multiple egocentric HOI sources | Open-vocabulary hand-object interaction understanding | open |
 | [EgoPlan-Bench](https://github.com/ChenYi99/EgoPlan) | 2023-12 | arXiv | Egocentric planning tasks | Multimodal LLM planning over human-level egocentric scenarios | open |
 | [Egocentric Pedestrian Trajectory Benchmark](https://arxiv.org/abs/2310.10424) | 2023-10 | arXiv | Egocentric pedestrian video | Trajectory prediction with scale- and motion-aware evaluation | watch |
 | [RefEgo](https://github.com/shuheikurita/RefEgo) | 2023-08 | ICCV 2023 | Ego4D | 12K+ clips / 41 hours for first-person referring-expression comprehension and referred-object tracking | open |
 | [EgoSchema](http://egoschema.github.io/) | 2023-08 | NeurIPS 2023 | Ego4D | Very-long-form multiple-choice video QA | open |
 | [Fine-Grained Affordance Annotation](https://arxiv.org/abs/2302.03292) | 2023-02 | WACV 2023 | Egocentric HOI videos | Fine-grained affordance labels for hand-object interaction | watch |
+| [Ego-Exo4D Benchmarks](https://ego-exo4d-data.org/) | 2023-11 | project page | Ego-Exo4D | Fine-grained activity, proficiency, cross-view translation, 3D pose, object correspondence | benchmark |
+| [EPIC-Sounds](https://epic-kitchens.github.io/epic-sounds/) | 2023-02 | ICASSP 2023 | EPIC-KITCHENS | Audio event recognition in egocentric kitchen video | open |
+| [EPIC-Fields](https://epic-kitchens.github.io/epic-fields/) | 2023-06 | NeurIPS 2023 | EPIC-KITCHENS | 3D fields and scene-level spatial reasoning over kitchen video | open |
 | [EgoClip / EgoMCQ](https://github.com/showlab/EgoVLP) | 2022-06 | NeurIPS 2022 | Ego4D | 3.8M clip-text pairs and MCQ development benchmark for egocentric VLP | open |
+| [VISOR](https://epic-kitchens.github.io/VISOR/) | 2022-09 | NeurIPS 2022 | EPIC-KITCHENS | Manual and dense masks, hand/object segmentation, active object relations | open |
 | [AssistSR](https://arxiv.org/abs/2111.15050) | 2021-11 | arXiv | Instructional daily-item video segments | Task-oriented question-driven video segment retrieval for personal assistants | watch |
+| [Ego4D Benchmarks](https://ego4d-data.org/) | 2021-10 | project page | Ego4D | Natural Language Query, Moment Query, episodic memory, state change, long-term anticipation, social/audio, hand-object | benchmark |
+| [TREK-150](https://machinelearning.uniud.it/datasets/trek150/) | 2021-08 | project page | EPIC-KITCHENS | Egocentric single-object tracking | open |
+| [EPIC-KITCHENS Challenges](https://epic-kitchens.github.io/) | 2018-04 | project page | EPIC-KITCHENS / EPIC-KITCHENS-100 | Recognition, detection, anticipation, retrieval, domain adaptation | benchmark |
 ## Models, Tools, and Baselines
 | Resource | Released | Venue | What it contributes | Link |
 | :--- | :---: | :---: | :--- | :---: |
 | UNIEGO | 2026-06 | arXiv | Unified egocentric encoder distilled from nine teachers spanning ego-exo views, RGB, depth, skeleton, and foundation-model representations | [Paper](https://arxiv.org/abs/2606.20559) |
+| ActiveMimic | 2026-06 | arXiv | Egocentric human-video pretraining with active-perception signals for manipulation and VLA transfer | [Paper](https://arxiv.org/abs/2606.06194) |
+| Continual Child-View Learning | 2026-06 | arXiv | Chronological multimodal learning from a child's egocentric video and speech stream | [Paper](https://arxiv.org/abs/2606.05115) |
+| Objects Before Words | 2026-06 | arXiv | Object-first language grounding from child-view egocentric video | [Paper](https://arxiv.org/abs/2606.12985) |
+| Watch Remember Reason | 2026-06 | arXiv | Human-view long-video understanding framework for MLLM watching, memory, and reasoning | [Paper](https://arxiv.org/abs/2606.07433) |
 | PhysBrain | 2025-12 | arXiv | Uses human egocentric data to bridge vision-language models toward physical intelligence and embodied control | [Paper](https://arxiv.org/abs/2512.16793) |
 | EgoM2P | 2025-06 | ICCV 2025 | Egocentric multimodal multitask pretraining over RGB, depth, gaze, and camera pose | [Paper](https://arxiv.org/abs/2506.07886) |
 | Exo2Ego / Ego-ExoClip | 2025-03 | AAAI 2026 | Transfers exocentric MLLM knowledge into egocentric video understanding with 1.1M synchronized ego-exo clip-text pairs and EgoIT instruction tuning | [Paper](https://arxiv.org/abs/2503.09143) |
 | EgoDistill | 2023-01 | tech report | Distills heavy egocentric clip features into efficient models using head-motion signals | [Project](https://vision.cs.utexas.edu/projects/egodistill/) |
 | EgoT2 / Egocentric Video Task Translation | 2022-12 | CVPR 2023 Highlight | Task-translation framework that transfers supervision between egocentric video tasks | [Project](https://vision.cs.utexas.edu/projects/egot2/) |
+### Video-Language and Long-Video Models
 | Resource | Released | Venue | What it contributes | Link |
 | :--- | :---: | :---: | :--- | :---: |
+| Temporal Action Graphs for Ego VLMs | 2026-06 | arXiv | Converts egocentric videos into narratives and temporal action graphs for in-context action recognition with open-weight VLMs | [Paper](https://arxiv.org/abs/2606.15417) |
+| ReRe Cross-View Revisiting | 2026-06 | ICML 2026 | Training-free spatial reasoning that revisits egocentric conclusions through synthesized complementary novel-view videos | [Project](https://zhenjiemao.github.io/ReRe/) |
+| CASTLE KG Retrieval | 2026-06 | CVPR 2026 EgoVis | Agentic long-context video understanding with video knowledge graphs and hierarchical retrieval for the CASTLE challenge | [Paper](https://arxiv.org/abs/2606.01933) |
 | AnchorWorld | 2026-06 | arXiv | Embodied egocentric world simulation with view-based evolution customization | [Paper](https://arxiv.org/abs/2606.07326) |
 | Understanding-Enhanced Ego Mistake Detection | 2026-06 | arXiv | Small/large-model collaboration for detecting incorrect procedural actions in egocentric video | [Paper](https://arxiv.org/abs/2606.02120) |
 | CASTLE2026 Team WDL | 2026-05 | CVPR 2026 EgoVis | Evidence-aware multimodal reasoning pipeline for long-form CASTLE egocentric QA | [Paper](https://arxiv.org/abs/2606.00712) |
 | CuriosAI CASTLE | 2026-05 | CVPR 2026 EgoVis | Search-verify-answer CASTLE challenge pipeline using timelines, transcripts, and VLM captions | [Paper](https://arxiv.org/abs/2605.27800) |
+| MARS CASTLE | 2026-05 | CVPR 2026 EgoVis | Multimodal agentic reasoning with source selection for CASTLE challenge QA | [Paper](https://arxiv.org/abs/2605.18176) |
+| OSGNet + MLLM Reranking | 2026-05 | CVPR 2026 EgoVis | Champion Ego4D Episodic Memory Challenge solution for NLQ and GoalStep using MLLM reranking over OSGNet candidates | [GitHub](https://github.com/iLearn-Lab/CVPR25-OSGNet) |
+| OmniEgo-R2 | 2026-05 | CVPR 2026 EgoVis | Routed reasoning framework for EgoCross; second place in both Source-Limited and Open-Source tracks | [GitHub](https://github.com/Lee-zixu/OmniEgo-R2) |
+| Reflective Dialogue EgoCross | 2026-05 | CVPR 2026 EgoVis | Inference-time Teacher/Solver reflective dialogue for EgoCross support-set adaptation without fine-tuning | [Paper](https://arxiv.org/abs/2605.27885) |
 | EgoCross Domain-Wise Inference | 2026-05 | CVPR 2026 EgoVis | Nearly training-free source-limited inference strategy for EgoCross domain shift | [Paper](https://arxiv.org/abs/2606.00829) |
 | HD-EPIC Semantic-Visual Evidence | 2026-05 | CVPR 2026 EgoVis | HD-EPIC VQA challenge solution separating semantic and visual evidence | [Paper](https://arxiv.org/abs/2605.29402) |
 | HiERO-StepG | 2026-05 | CVPR 2026 EgoVis | Hierarchical activity-understanding solution for the Ego4D Step Grounding Challenge | [Paper](https://arxiv.org/abs/2605.31227) |
 | TempRet | 2026-05 | CVPR 2026 EgoVis | Temporal enhancement and reranking for EPIC-KITCHENS-100 multi-instance retrieval | [Paper](https://arxiv.org/abs/2605.24470) |
 | Trajectory-Conditioned Egocentric Prediction | 2026-05 | arXiv | Future ego-view prediction conditioned on camera trajectory to disambiguate action outcomes | [Paper](https://arxiv.org/abs/2605.20388) |
+| EgoSim | 2026-04 | arXiv | Closed-loop egocentric world simulator with 3D grounding and dynamic state updates for multi-stage interaction generation | [Paper](https://arxiv.org/abs/2604.01001) |
+| EgoMotion | 2026-04 | arXiv | Hierarchical reasoning + diffusion for egocentric vision-language motion generation | [Paper](https://arxiv.org/abs/2604.19105) |
 | Ego-InBetween | 2026-04 | arXiv | Generates object state transitions in ego-centric videos from action instructions | [Paper](https://arxiv.org/abs/2604.17749) |
 | Syn2Seq Exo-to-Ego | 2026-04 | arXiv | Sequential exo-to-ego video generation from synchronized third-person views and camera poses | [Paper](https://arxiv.org/abs/2604.13793) |
 | UniversalVTG | 2026-04 | arXiv | Lightweight cross-dataset foundation model for video temporal grounding | [Paper](https://arxiv.org/abs/2604.08522) |
 | V-Nutri | 2026-04 | arXiv | Dish-level nutrition estimation from egocentric cooking videos | [Paper](https://arxiv.org/abs/2604.11913) |
 | LOME | 2026-03 | arXiv | Action-conditioned egocentric world model generating photorealistic human-object interactions from image, text, and per-frame actions | [Paper](https://arxiv.org/abs/2603.27449) |
 | Temporal-Aware Ego VLM | 2026-03 | arXiv | Training scheme that incentivizes temporal awareness in egocentric video-understanding models | [Paper](https://arxiv.org/abs/2603.27184) |
 | EgoForge | 2026-03 | arXiv | Goal-directed egocentric world simulator that rolls out first-person video from a single image and a high-level instruction | [Paper](https://arxiv.org/abs/2603.20169) |
+| EgoReasoner | 2026-03 | arXiv | Task-adaptive structured thinking for egocentric 4D spatial and object reasoning | [Paper](https://arxiv.org/abs/2603.06561) |
+| Gaze-Regularized VLMs | 2026-03 | arXiv | Gaze-conditioned VLM training for ego-centric behavior understanding | [Paper](https://arxiv.org/abs/2603.23190) |
+| Recurrent Reasoning VLM | 2026-03 | arXiv | Recurrent VLM reasoning for long-horizon embodied task-progress estimation | [Paper](https://arxiv.org/abs/2603.17312) |
+| Ropedia Xperience-10M Task Baselines | 2026-03 | Hugging Face | Public Xperience-10M sample task definitions, baselines, metrics, and scale-up evaluation notes | [Hugging Face](https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines) |
+| EgoMAS | 2026-03 | arXiv | Shared-memory baseline for multi-agent egocentric video QA | [Project](https://ma-egoqa.github.io/) |
 | DreamDojo | 2026-02 | ICML 2026 | Generalist robot world model pretrained on 44K hours of egocentric human video with continuous latent actions, distilled to real time | [GitHub](https://github.com/NVIDIA/DreamDojo) |
 | Hand2World | 2026-02 | arXiv | Autoregressive egocentric world model generating first-person interaction video from free-space hand gestures | [Paper](https://arxiv.org/abs/2602.09600) |
 | Edge Episodic Memory QA | 2026-02 | arXiv | On-device dual-thread MLLM for real-time egocentric episodic-memory QA (QAEgo4D-Closed) on the edge | [Paper](https://arxiv.org/abs/2602.22455) |
 | EgoGraph | 2026-02 | arXiv | Training-free temporal knowledge graph for ultra-long egocentric video understanding | [Paper](https://arxiv.org/abs/2602.23709) |
 | EGAgent | 2026-01 | arXiv | Entity-scene-graph agent for very-long egocentric video understanding over all-day wearable streams | [GitHub](https://github.com/facebookresearch/egagent) |
 | Walk through Paintings | 2026-01 | arXiv | Egocentric world models from internet priors that generate first-person scene walkthroughs | [Paper](https://arxiv.org/abs/2601.15284) |
+| Event-VStream | 2026-01 | arXiv | Event-driven long-video stream understanding for real-time video-language systems | [Paper](https://arxiv.org/abs/2601.15655) |
+| HD-EPIC VQA T-CoT | 2026-01 | arXiv | HD-EPIC VQA solution with temporal chain-of-thought prompting and Qwen2.5-VL adaptation | [Paper](https://arxiv.org/abs/2601.10228) |
+| Robust Egocentric Visual Attention | 2026-01 | arXiv | Language-guided scene-context model for egocentric visual attention prediction | [Paper](https://arxiv.org/abs/2601.01818) |
 | EgoControl | 2025-11 | arXiv | Pose-controllable egocentric video diffusion conditioned on sequences of 3D full-body poses | [Paper](https://arxiv.org/abs/2511.18173) |
 | DMC3 Ego VideoQA | 2025-10 | arXiv | Counterfactual contrastive construction for egocentric VideoQA across event descriptions and hand-object interaction cues | [Paper](https://arxiv.org/abs/2510.20285) |
 | EgoThinker | 2025-10 | NeurIPS 2025 | Egocentric reasoning model with spatio-temporal chain-of-thought and RL fine-tuning | [Paper](https://arxiv.org/abs/2510.23569) |
 | HumanScale | 2026-06 | arXiv | Shows filtered, labeled egocentric human video can outperform teleoperated real-robot data for embodied pretraining | [Paper](https://arxiv.org/abs/2606.20521) |
 | Do as I Do | 2026-06 | arXiv | Retargets human hand-object interactions from in-the-wild monocular video into executable dexterous robot trajectories | [Paper](https://arxiv.org/abs/2606.19333) |
 | Motion-Focused Latent Action VLA | 2026-06 | IROS 2026 | Extracts motion-focused latent actions from unlabeled human EgoVideos for cross-embodiment VLA pretraining and adaptation | [Paper](https://arxiv.org/abs/2606.18955) |
+| EgoPriMo | 2026-06 | arXiv | Egocentric motion prior for interactive humanoid control from human demonstrations | [Paper](https://arxiv.org/abs/2606.08495) |
 | EgoPhys | 2026-06 | arXiv | Learns deformable-object physics digital twins from egocentric RGB interaction video for robot planning | [Project](https://hjhyunjinkim.github.io/EgoPhys) |
 | EDITH | 2026-06 | arXiv | Streams first-person view, gaze, and speech from smart glasses into hierarchical robot policies for natural HRI | [Project](https://project-edith.github.io) |
 | Divide Deliberate Decide | 2026-06 | arXiv | Local zero-shot multi-agent VLM framework for fine-grained egocentric action recognition | [Paper](https://arxiv.org/abs/2606.17627) |
 | ACE-Ego-0 | 2026-06 | arXiv | Unifies egocentric human video with robot/sim data via reliability-aware weighting for VLA pretraining (RoboCasa GR1, RoboTwin 2.0) | [Paper](https://arxiv.org/abs/2606.17200) |
 | EgoAction | 2026-05 | CVPR 2026 | CVPR 2026 EPIC-KITCHENS action detection challenge pipeline | [Paper](https://arxiv.org/abs/2605.24496) |
 | EgoAdapt | 2026-05 | CVPR 2026 | CVPR 2026 HD-EPIC VQA challenge inference-time adaptation pipeline | [Paper](https://arxiv.org/abs/2605.24500) |
+| FROST-STA | 2026-05 | CVPR 2026 EgoVis | Frozen dense-feature Ego4D short-term object-interaction anticipation submission | [Paper](https://arxiv.org/abs/2606.00694) |
+| JFAA | 2026-05 | CVPR 2026 EgoVis | JEPA-based EPIC-KITCHENS-100 action-anticipation challenge submission | [Paper](https://arxiv.org/abs/2605.20904) |
+| Mamba Ego Action Recognition | 2026-05 | CVPR 2026 EgoVis | Cross-modal egocentric action recognition using RGB and hand-skeleton streams with Mamba | [Paper](https://arxiv.org/abs/2605.24302) |
+| TAP-JEPA | 2026-05 | CVPR 2026 EgoVis | EPIC-KITCHENS-100 action-anticipation runner-up using frozen V-JEPA 2.1 features | [Paper](https://arxiv.org/abs/2606.00662) |
+| VISTA | 2026-05 | CVPR 2026 EgoVis | V-JEPA plus StillFast temporal anticipator for Ego4D STA at EgoVis 2026 | [Paper](https://arxiv.org/abs/2605.20901) |
+| WristCompass | 2026-05 | arXiv | Learns ego-camera orientation from hand/camera kinematic coupling in manipulation video | [Paper](https://arxiv.org/abs/2605.30671) |
+| Zero-Shot Ego Object ReID | 2026-05 | arXiv | SAM3-feature fusion for zero-shot object re-identification in egocentric kitchen videos | [Paper](https://arxiv.org/abs/2605.26383) |
+| EgoRelight | 2026-05 | arXiv | HMD-based egocentric human capture and illumination recovery for relightable avatars | [Paper](https://arxiv.org/abs/2605.28401) |
 | StableHand | 2026-05 | arXiv | Quality-aware flow-matching baseline for world-space dual-hand motion estimation from egocentric video | [Project](https://huajian-zeng.github.io/projects/stablehand/) |
 | HumanEgo | 2026-05 | arXiv | Zero-shot robot learning from minutes of human egocentric videos via entity-level hand-object representations and flow-matching policies | [Project](https://humanego-ai.github.io) |
 | EgoForce Hand Pose | 2026-05 | SIGGRAPH 2026 | Monocular egocentric 3D hand pose and shape reconstruction across fisheye, perspective, and wide-FOV camera models | [Project](https://dfki-av.github.io/EgoForce) |
 | EgoExo-WM | 2026-05 | arXiv | Converts exocentric video into egocentric world-model training data using body-pose priors | [Project](https://vision.cs.utexas.edu/projects/EgoExo-WM/) |
 | MotionGRPO | 2026-05 | ICML 2026 | GRPO-based post-training for full-body 3D motion recovery from head-mounted device signals | [Paper](https://arxiv.org/abs/2605.05680) |
 | ActiveGlasses | 2026-04 | arXiv | Learns robot manipulation from smart-glasses ego-centric human demonstrations and transfers active vision to a robot perception arm | [Paper](https://arxiv.org/abs/2604.08534) |
+| Personal Point of View 3DGS | 2026-04 | arXiv | Evaluation of dynamic 3D Gaussian splatting for egocentric scene reconstruction | [Paper](https://arxiv.org/abs/2604.23803) |
+| VGGT-Segmentor | 2026-04 | arXiv | Geometry-enhanced segmentation across egocentric and exocentric views | [Paper](https://arxiv.org/abs/2604.13596) |
 | Gaze-SoM HOI Anticipation | 2026-04 | arXiv | Gaze and set-of-mark prompting in VLLMs for hand-object-interaction anticipation from egocentric video | [Paper](https://arxiv.org/abs/2604.03667) |
 | EgoFlow | 2026-04 | arXiv | Gradient-guided flow matching for physically plausible 6DoF object-motion generation from egocentric video | [Paper](https://arxiv.org/abs/2604.01421) |
 | UniDex | 2026-03 | arXiv | Robot foundation suite for universal dexterous hand control learned from egocentric human videos | [Paper](https://arxiv.org/abs/2603.22264) |
+| PAWS | 2026-03 | arXiv | Articulation extraction from large-scale hand-object interactions in egocentric video | [Paper](https://arxiv.org/abs/2603.25539) |
+| Static Scene Reconstruction from Dynamic Egocentric Videos | 2026-03 | arXiv | Mask-aware 3D reconstruction pipeline for long-form dynamic egocentric video | [Paper](https://arxiv.org/abs/2603.22450) |
 | EgoHOI World Model | 2026-03 | arXiv | Physics-informed egocentric world model that synthesizes contact-consistent hand-object interactions from action signals alone | [Paper](https://arxiv.org/abs/2603.13615) |
 | STAformer++ Affordance-Aware Anticipation | 2026-02 | arXiv | Integrates temporal attention, scene affordance memory, and interaction hotspots for short-term object-interaction anticipation on Ego4D and EPIC-KITCHENS | [Paper](https://arxiv.org/abs/2602.14837) |
 | Neck-Mounted Gaze (GLC) | 2026-02 | arXiv | Transformer gaze estimator for a shoulder-level neck-mounted camera with out-of-bound classification and multi-view co-learning | [Paper](https://arxiv.org/abs/2602.11669) |
 | Diff-IP2D | 2024-05 | arXiv | Non-autoregressive diffusion forecasting of 2D hand trajectories and object affordances with camera-egomotion conditioning | [GitHub](https://github.com/IRMVLab/Diff-IP2D) |
 | EffHandEgoNet | 2024-04 | FG 2024 | Egocentric 2D hand pose and action-recognition method for smart-glasses RGB input on H2O and FPHA | [Paper](https://arxiv.org/abs/2404.09308) |
 | EgoLifter | 2024-03 | ECCV 2024 | Open-world 3D segmentation decomposing natural egocentric video into individual 3D objects via 3D Gaussians and SAM | [GitHub](https://github.com/facebookresearch/egolifter) |
+| EgoPoseFormer | 2024-03 | arXiv | Transformer baseline for stereo egocentric 3D human pose estimation | [GitHub](https://github.com/ChenhongyiYang/egoposeformer) |
 | Get a Grip | 2023-12 | arXiv | Reconstructs stable hand-object grasps from egocentric video | [Project](https://zhifanzhu.github.io/getagrip) |
 | Aria-NeRF | 2023-11 | arXiv | Multimodal egocentric view synthesis for Project Aria-style capture | [Paper](https://arxiv.org/abs/2311.06455) |
 | Egocentric Whole-Body MoCap | 2023-11 | arXiv | FisheyeViT plus diffusion refinement for egocentric whole-body motion capture | [Paper](https://arxiv.org/abs/2311.16495) |
 | TransFusion | 2023-01 | IEEE TCSVT 2024 | Summarizes past egocentric context in language to improve multimodal object-interaction anticipation | [Paper](https://arxiv.org/abs/2301.09209) |
 | Ego-Only | 2023-01 | arXiv | Egocentric action detection without exocentric pretraining transfer | [Paper](https://arxiv.org/abs/2301.01380) |
 | EgoSTARK | 2023-01 | arXiv | Adapted long-term tracker baseline for EgoTracks | [Paper](https://arxiv.org/abs/2301.03213) |
+| AV-CONV | 2023-12 | arXiv | Audio-visual conversational graph prediction from ego/exo conversation | [Project](https://vjwq.github.io/AV-CONV/) |
 | EgoLoc | 2022-12 | ICCV 2023 | Stronger Ego4D visual-query 3D object localization with camera-pose and localization baselines | [Paper](https://arxiv.org/abs/2212.06969) |
 | CONE | 2022-11 | ECCV 2022 Workshop | Coarse-to-fine alignment framework for Ego4D Natural Language Queries | [Paper](https://arxiv.org/abs/2211.08776) |
 | InternVideo-Ego4D | 2022-11 | Ego4D Workshop 2022 | Pack of champion Ego4D challenge solutions spanning episodic memory, forecasting, hand-object, and audio/social tracks | [Paper](https://arxiv.org/abs/2211.09529) |
 | HOI Forecast / Interaction Hotspots | 2022-04 | CVPR 2022 | Forecasts future hand trajectories and interaction hotspots on next-active objects | [Project](https://stevenlsw.github.io/hoi-forecast) |
 | EgoGAN | 2022-03 | arXiv | Generative future hand-mask forecasting from egocentric video | [Paper](https://arxiv.org/abs/2203.11305) |
 | Untrimmed Action Anticipation | 2022-02 | arXiv | Reframes egocentric anticipation for untrimmed first-person streams | [Paper](https://arxiv.org/abs/2202.04132) |
+| EgoHOS model | 2022-08 | GitHub | Context-aware hand-object segmentation and augmentation pipeline | [GitHub](https://github.com/owenzlz/EgoHOS) |
 | E2(GO)MOTION | 2021-12 | CVPR 2022 | Motion-augmented event-stream representation for egocentric action recognition | [Paper](https://arxiv.org/abs/2112.03596) |
 | Temporal-Context Ego Action Recognition | 2021-11 | BMVC 2021 | Multimodal transformer that uses surrounding temporal context for egocentric action recognition | [Paper](https://arxiv.org/abs/2111.01024) |
 | Exo-to-Ego Video Synthesis | 2021-07 | ACM MM 2021 | Cross-view synthesis model that generates egocentric video from exocentric video | [Paper](https://arxiv.org/abs/2107.03120) |
 | Future Person Localization | 2017-11 | CVPR 2018 | Predicts where people will appear in future frames from a first-person wearable camera | [Paper](https://arxiv.org/abs/1711.11217) |
 | First-Person Activity Forecasting | 2016-12 | ICCV 2017 Oral | DARKO online inverse-reinforcement-learning system for forecasting actions and active objects | [Paper](https://arxiv.org/abs/1612.07796) |
 | Egocentric FOV Localization | 2015-10 | WACV 2015 | Localizes the camera wearer's first-person field of view in overhead/surveillance video | [Paper](https://arxiv.org/abs/1510.02073) |
+| First-Person Pose Recognition | 2015-06 | CVPR 2015 | Uses egocentric workspaces and camera geometry for first-person human pose recognition | [Paper](https://openaccess.thecvf.com/content_cvpr_2015/html/Rogez_First-Person_Pose_Recognition_2015_CVPR_paper.html) |
 ### Practical Tooling
 | Tool | Released | Venue | Use |
 | :--- | :---: | :---: | :--- |
+| [OpenGlass](https://arxiv.org/abs/2606.07431) | 2026-06 | arXiv | Low-power open AI eyewear platform with event-based vision support for on-device smart-glasses research. |
 | [EgoKit](https://egokit.chuange.org/) | 2026-05 | arXiv | Low-cost synchronized ego/wrist recording workflow across phones, smart glasses, and XR hosts. |
+| [GBAT](https://arxiv.org/abs/2605.22962) | 2026-05 | arXiv | Annotating egocentric eye-tracking and video in child-caregiver interaction studies. |
 | [MobileEgo Anywhere](https://arxiv.org/abs/2605.05945) | 2026-05 | arXiv | Commodity-phone infrastructure for hour-plus egocentric trajectories, STERA processing, and long-form VLA data collection. |
 | [VisionClaw](https://arxiv.org/abs/2604.03486) | 2026-04 | arXiv | Always-on wearable AI-agent system on Ray-Ban Meta smart glasses, coupling live egocentric perception with speech-driven task execution. |
+| [HOMIE-toolkit](https://github.com/Ropedia/HOMIE-toolkit) | 2026-03 | GitHub | Loading and visualizing Xperience-10M HDF5 annotations, calibration, SLAM, hand/body mocap, depth, IMU, and point clouds. |
 | [RaycastGrasp](https://arxiv.org/abs/2510.22113) | 2025-10 | ICIR 2025 | Egocentric gaze-guided MR headset interface for robotic object retrieval and manipulation. |
 | [AiGet](https://arxiv.org/abs/2501.16240) | 2025-01 | CHI 2025 | Smart-glasses assistant for gaze/context/profile-driven informal learning during everyday moments. |
 | [HUX](https://arxiv.org/abs/2407.19492) | 2024-07 | arXiv | Always-on smart-glasses / XR companion concept with gaze, environment context, verbal context, and memory storage. |
+| [HOT3D tooling](https://facebookresearch.github.io/hot3d/) | 2024-06 | project page | Loading HOT3D hand/object/camera pose annotations and models. |
 | [EgoBlur](https://arxiv.org/abs/2308.13093) | 2023-08 | arXiv | Privacy-preserving blur pipeline and responsible-innovation analysis for Project Aria egocentric capture. |
+| [Ego-Exo4D CLI and docs](https://ego-exo4d-data.org/) | 2023-11 | project page | Downloading synchronized ego-exo data and annotations. |
+| [EgoObjects API](https://github.com/facebookresearch/EgoObjects) | 2023-09 | arXiv | Working with category and instance-level egocentric object labels. |
+| [Project Aria Tools](https://github.com/facebookresearch/projectaria_tools) | 2023-08 | arXiv | Reading Aria VRS, calibration, MPS, trajectory, gaze, and dataset artifacts. |
+| [VISOR API](https://github.com/epic-kitchens/VISOR) | 2022-09 | GitHub | Loading dense EPIC-KITCHENS hand/object masks and relations. |
+| [HOI4D tooling](https://hoi4d.github.io/) | 2022-03 | project page | Loading RGB-D frames, point clouds, object meshes, and pose/segmentation annotations. |
 | [PAL](https://arxiv.org/abs/2105.10735) | 2021-05 | CVPR 2021 EPIC Workshop | Wearable personalized visual-context detection for privacy-preserving intelligence augmentation. |
+| [Ego4D CLI and docs](https://ego4d-data.org/) | 2021-10 | project page | Downloading and working with Ego4D data after license approval. |
 ## Adjacent and Related Resources
 ## Contributing
+Contributions are welcome through pull requests and issues. Please include an official source, a concise description of what the resource contributes, and any known access or license notes.
+See [`CONTRIBUTING.md`](CONTRIBUTING.md) for the inclusion policy and style. Automated checks run in CI to keep the README, catalog data, figures, and public exports consistent; contributors do not need to run local scripts before opening a PR.
 ## Cite This Atlas

README.pt.md CHANGED Viewed

@@ -18,9 +18,9 @@
 <h1 align="center">Awesome Egocentric Atlas</h1>
-<p align="center"><strong>Um mapa curado da IA em primeira pessoa: os conjuntos de dados, benchmarks, modelos e ferramentas por trás da visão egocêntrica, da IA incorporada e da robótica, da aprendizagem visão-linguagem, da memória de longo contexto, da RA/RV e da interação mão-objeto.</strong></p>
-<p align="center"><strong>277</strong> recursos egocêntricos — 104 conjuntos de dados · 69 benchmarks · 90 modelos · 13 ferramentas</p>
 ## O que inclui

 <h1 align="center">Awesome Egocentric Atlas</h1>
+<p align="center"><strong>Um mapa curado da IA egocêntrica: os conjuntos de dados, benchmarks, modelos e ferramentas por trás da visão egocêntrica, da IA incorporada e da robótica, da aprendizagem visão-linguagem, da memória de longo contexto, da RA/RV e da interação mão-objeto.</strong></p>
+<p align="center"><strong>456</strong> recursos egocêntricos — 125 conjuntos de dados · 81 benchmarks · 226 modelos · 23 ferramentas</p>
 ## O que inclui

README.zh.md CHANGED Viewed

@@ -18,9 +18,9 @@
 <h1 align="center">Awesome Egocentric Atlas</h1>
-<p align="center"><strong>第一人称 AI 的精选地图——汇集自我中心视觉、具身智能与机器人、视频语言、长上下文记忆、AR/VR 以及手物交互背后的数据集、基准、模型与工具。</strong></p>
-<p align="center"><strong>277</strong> 自我中心资源 — 104 数据集 · 69 基准 · 90 模型 · 13 工具包</p>
 ## 内容概览

 <h1 align="center">Awesome Egocentric Atlas</h1>
+<p align="center"><strong>自我中心 AI 的精选地图——汇集自我中心视觉、具身智能与机器人、视频语言、长上下文记忆、AR/VR 以及手物交互背后的数据集、基准、模型与工具。</strong></p>
+<p align="center"><strong>456</strong> 自我中心资源 — 125 数据集 · 81 基准 · 226 模型 · 23 工具包</p>
 ## 内容概览

app.js CHANGED Viewed

@@ -13,11 +13,11 @@ const I18N = {
     skip: "Skip to catalog",
     "nav.catalog": "Catalog", "nav.lanes": "Lanes", "nav.access": "Access", "nav.readme": "README", "nav.milestones": "Milestones",
     "milestones.title": "Milestones", "milestones.desc": "The landmark works that shaped egocentric AI — from the field's origins to its current frontier.",
-    "hero.lead": "A curated map of first-person AI — the datasets, benchmarks, models, and tools behind egocentric vision, embodied AI and robotics, video-language, long-context memory, AR/VR, and hand-object interaction.",
     "btn.github": "GitHub Repo", "btn.hf": "Hugging Face Mirror", "btn.browse": "Browse Catalog",
     "stat.resources": "egocentric resources", "stat.datasets": "datasets", "stat.benchmarks": "benchmarks", "stat.models": "models", "stat.toolkits": "toolkits",
     "proof.1": "Datasets, benchmarks, models & tools", "proof.2": "Filter by task, status, and date", "proof.3": "Open access, MIT licensed",
-    "media.caption": "First-person AI, mapped",
     "catalog.title": "Catalog Explorer", "catalog.desc": "Search by name, task, modality, status, kind, or research lane.", "catalog.openyaml": "Open YAML",
     "filter.search": "Search", "filter.kind": "Kind", "filter.status": "Status", "filter.lane": "Lane", "filter.reset": "Reset",
     "filter.allkinds": "All kinds", "filter.allstatuses": "All statuses", "filter.alllanes": "All lanes",
@@ -26,7 +26,7 @@ const I18N = {
     loading: "Loading catalog...", "note.default": "Newest matches first",
     "count.matching": "{n} matching resources", "note.showing": "Showing newest {a} of {b}", "note.showingall": "Showing all {a}",
     "empty.title": "No resources match those filters.", "empty.hint": "Try a broader search, remove a lane, or reset the filters.",
-    "lanes.title": "Research Lanes", "lanes.desc": "Six entry points for the main ways people use first-person data.", "lanes.taxonomy": "Taxonomy",
     "access.title": "Access Reality", "access.desc": "See what you can download today versus what needs a request, is benchmark-only, partial, or still unverified — before you plan experiments.",
     "maintain.title": "Add a Resource", "maintain.desc": "Found something we're missing? Suggest it in a few steps — open a short issue and it flows into the catalog.", "maintain.add": "Add Resource",
     "maintain.contributing": "Contributing guide", "maintain.schema": "Resource schema", "maintain.status": "Status policy", "maintain.workflow": "Maintenance workflow",
@@ -39,11 +39,11 @@ const I18N = {
     skip: "跳到目录",
     "nav.catalog": "目录", "nav.lanes": "方向", "nav.access": "可获取性", "nav.readme": "README", "nav.milestones": "里程碑",
     "milestones.title": "里程碑", "milestones.desc": "塑造自我中心 AI 的里程碑工作——从领域起源到当前前沿。",
-    "hero.lead": "第一人称 AI 的精选地图——汇集自我中心视觉、具身智能与机器人、视频语言、长上下文记忆、AR/VR 以及手物交互背后的数据集、基准、模型与工具。",
     "btn.github": "GitHub 仓库", "btn.hf": "Hugging Face 镜像", "btn.browse": "浏览目录",
     "stat.resources": "自我中心资源", "stat.datasets": "数据集", "stat.benchmarks": "基准", "stat.models": "模型", "stat.toolkits": "工具包",
     "proof.1": "数据集、基准、模型与工具", "proof.2": "按任务、状态与日期筛选", "proof.3": "开放获取，MIT 许可",
-    "media.caption": "第一人称 AI 全景",
     "catalog.title": "目录浏览器", "catalog.desc": "按名称、任务、模态、状态、类型或研究方向搜索。", "catalog.openyaml": "打开 YAML",
     "filter.search": "搜索", "filter.kind": "类型", "filter.status": "状态", "filter.lane": "方向", "filter.reset": "重置",
     "filter.allkinds": "全部类型", "filter.allstatuses": "全部状态", "filter.alllanes": "全部方向",
@@ -52,7 +52,7 @@ const I18N = {
     loading: "正在加载目录……", "note.default": "最新优先",
     "count.matching": "{n} 个匹配资源", "note.showing": "显示最新 {a} / 共 {b}", "note.showingall": "共显示 {a} 个",
     "empty.title": "没有资源符合这些筛选条件。", "empty.hint": "尝试更宽泛的搜索、移除某个方向，或重置筛选。",
-    "lanes.title": "研究方向", "lanes.desc": "六个入口，覆盖使用第一人��数据的主要方式。", "lanes.taxonomy": "分类体系",
     "access.title": "可获取性一览", "access.desc": "在规划实验前，先看清哪些今天即可下载，哪些需要申请、仅为基准、部分公开，或尚待核实。",
     "maintain.title": "添加资源", "maintain.desc": "发现我们遗漏的资源？只需几步即可提交——开一个简短的 issue，它就会进入目录。", "maintain.add": "添加资源",
     "maintain.contributing": "贡献指南", "maintain.schema": "资源结构", "maintain.status": "状态政策", "maintain.workflow": "维护流程",
@@ -65,11 +65,11 @@ const I18N = {
     skip: "Saltar al catálogo",
     "nav.catalog": "Catálogo", "nav.lanes": "Áreas", "nav.access": "Acceso", "nav.readme": "README", "nav.milestones": "Hitos",
     "milestones.title": "Hitos", "milestones.desc": "Los trabajos de referencia que dieron forma a la IA egocéntrica, desde sus orígenes hasta la frontera actual.",
-    "hero.lead": "Un mapa curado de la IA en primera persona: los conjuntos de datos, benchmarks, modelos y herramientas tras la visión egocéntrica, la IA encarnada y la robótica, el aprendizaje visión-lenguaje, la memoria de largo contexto, la RA/RV y la interacción mano-objeto.",
     "btn.github": "Repositorio GitHub", "btn.hf": "Espejo en Hugging Face", "btn.browse": "Explorar catálogo",
     "stat.resources": "recursos egocéntricos", "stat.datasets": "conjuntos de datos", "stat.benchmarks": "benchmarks", "stat.models": "modelos", "stat.toolkits": "herramientas",
     "proof.1": "Datos, benchmarks, modelos y herramientas", "proof.2": "Filtra por tarea, estado y fecha", "proof.3": "Acceso abierto, licencia MIT",
-    "media.caption": "La IA en primera persona, mapeada",
     "catalog.title": "Explorador del catálogo", "catalog.desc": "Busca por nombre, tarea, modalidad, estado, tipo o área de investigación.", "catalog.openyaml": "Abrir YAML",
     "filter.search": "Buscar", "filter.kind": "Tipo", "filter.status": "Estado", "filter.lane": "Área", "filter.reset": "Restablecer",
     "filter.allkinds": "Todos los tipos", "filter.allstatuses": "Todos los estados", "filter.alllanes": "Todas las áreas",
@@ -78,7 +78,7 @@ const I18N = {
     loading: "Cargando catálogo...", "note.default": "Primero los más recientes",
     "count.matching": "{n} recursos coincidentes", "note.showing": "Mostrando los {a} más recientes de {b}", "note.showingall": "Mostrando los {a}",
     "empty.title": "Ningún recurso coincide con esos filtros.", "empty.hint": "Prueba una búsqueda más amplia, quita un área o restablece los filtros.",
-    "lanes.title": "Áreas de investigación", "lanes.desc": "Seis puntos de entrada a las formas principales de usar datos en primera persona.", "lanes.taxonomy": "Taxonomía",
     "access.title": "Realidad de acceso", "access.desc": "Distingue lo que puedes descargar hoy de lo que requiere solicitud, es solo benchmark, es parcial o aún no está verificado, antes de planificar experimentos.",
     "maintain.title": "Añadir un recurso", "maintain.desc": "¿Falta algo? Propónlo en unos pocos pasos: abre una breve incidencia y entrará en el catálogo.", "maintain.add": "Añadir recurso",
     "maintain.contributing": "Guía de contribución", "maintain.schema": "Esquema de recursos", "maintain.status": "Política de estados", "maintain.workflow": "Flujo de mantenimiento",
@@ -91,11 +91,11 @@ const I18N = {
     skip: "Aller au catalogue",
     "nav.catalog": "Catalogue", "nav.lanes": "Axes", "nav.access": "Accès", "nav.readme": "README", "nav.milestones": "Jalons",
     "milestones.title": "Jalons", "milestones.desc": "Les travaux marquants qui ont façonné l'IA égocentrique, des origines du domaine à sa frontière actuelle.",
-    "hero.lead": "Une carte sélective de l'IA à la première personne : les jeux de données, benchmarks, modèles et outils derrière la vision égocentrique, l'IA incarnée et la robotique, l'apprentissage vision-langage, la mémoire à long contexte, la RA/RV et l'interaction main-objet.",
     "btn.github": "Dépôt GitHub", "btn.hf": "Miroir Hugging Face", "btn.browse": "Parcourir le catalogue",
     "stat.resources": "ressources égocentriques", "stat.datasets": "jeux de données", "stat.benchmarks": "benchmarks", "stat.models": "modèles", "stat.toolkits": "outils",
     "proof.1": "Données, benchmarks, modèles et outils", "proof.2": "Filtrer par tâche, statut et date", "proof.3": "Accès libre, licence MIT",
-    "media.caption": "L'IA à la première personne, cartographiée",
     "catalog.title": "Explorateur du catalogue", "catalog.desc": "Recherchez par nom, tâche, modalité, statut, type ou axe de recherche.", "catalog.openyaml": "Ouvrir le YAML",
     "filter.search": "Rechercher", "filter.kind": "Type", "filter.status": "Statut", "filter.lane": "Axe", "filter.reset": "Réinitialiser",
     "filter.allkinds": "Tous les types", "filter.allstatuses": "Tous les statuts", "filter.alllanes": "Tous les axes",
@@ -104,7 +104,7 @@ const I18N = {
     loading: "Chargement du catalogue...", "note.default": "Les plus récents d'abord",
     "count.matching": "{n} ressources correspondantes", "note.showing": "Affichage des {a} plus récentes sur {b}", "note.showingall": "Affichage des {a}",
     "empty.title": "Aucune ressource ne correspond à ces filtres.", "empty.hint": "Essayez une recherche plus large, retirez un axe ou réinitialisez les filtres.",
-    "lanes.title": "Axes de recherche", "lanes.desc": "Six points d'entrée vers les principales façons d'utiliser les données à la première personne.", "lanes.taxonomy": "Taxonomie",
     "access.title": "Réalité de l'accès", "access.desc": "Distinguez ce que vous pouvez télécharger aujourd'hui de ce qui nécessite une demande, est réservé aux benchmarks, partiel ou non vérifié, avant de planifier vos expériences.",
     "maintain.title": "Ajouter une ressource", "maintain.desc": "Une ressource manquante ? Proposez-la en quelques étapes : ouvrez un court ticket et elle rejoindra le catalogue.", "maintain.add": "Ajouter une ressource",
     "maintain.contributing": "Guide de contribution", "maintain.schema": "Schéma des ressources", "maintain.status": "Politique des statuts", "maintain.workflow": "Flux de maintenance",
@@ -117,11 +117,11 @@ const I18N = {
     skip: "Zum Katalog springen",
     "nav.catalog": "Katalog", "nav.lanes": "Bereiche", "nav.access": "Zugang", "nav.readme": "README", "nav.milestones": "Meilensteine",
     "milestones.title": "Meilensteine", "milestones.desc": "Die wegweisenden Arbeiten, die egozentrische KI geprägt haben – von den Ursprüngen bis zur aktuellen Front.",
-    "hero.lead": "Eine kuratierte Karte der First-Person-KI – die Datensätze, Benchmarks, Modelle und Werkzeuge hinter egozentrischem Sehen, verkörperter KI und Robotik, Video-Sprache, Langzeitgedächtnis, AR/VR und Hand-Objekt-Interaktion.",
     "btn.github": "GitHub-Repo", "btn.hf": "Hugging-Face-Spiegel", "btn.browse": "Katalog durchsuchen",
     "stat.resources": "egozentrische Ressourcen", "stat.datasets": "Datensätze", "stat.benchmarks": "Benchmarks", "stat.models": "Modelle", "stat.toolkits": "Toolkits",
     "proof.1": "Datensätze, Benchmarks, Modelle & Werkzeuge", "proof.2": "Nach Aufgabe, Status und Datum filtern", "proof.3": "Offen zugänglich, MIT-Lizenz",
-    "media.caption": "First-Person-KI, kartiert",
     "catalog.title": "Katalog-Explorer", "catalog.desc": "Suche nach Name, Aufgabe, Modalität, Status, Art oder Forschungsbereich.", "catalog.openyaml": "YAML öffnen",
     "filter.search": "Suche", "filter.kind": "Art", "filter.status": "Status", "filter.lane": "Bereich", "filter.reset": "Zurücksetzen",
     "filter.allkinds": "Alle Arten", "filter.allstatuses": "Alle Status", "filter.alllanes": "Alle Bereiche",
@@ -130,7 +130,7 @@ const I18N = {
     loading: "Katalog wird geladen...", "note.default": "Neueste zuerst",
     "count.matching": "{n} passende Ressourcen", "note.showing": "Zeige die neuesten {a} von {b}", "note.showingall": "Zeige alle {a}",
     "empty.title": "Keine Ressourcen passen zu diesen Filtern.", "empty.hint": "Versuche eine breitere Suche, entferne einen Bereich oder setze die Filter zurück.",
-    "lanes.title": "Forschungsbereiche", "lanes.desc": "Sechs Einstiegspunkte für die wichtigsten Wege, First-Person-Daten zu nutzen.", "lanes.taxonomy": "Taxonomie",
     "access.title": "Zugangslage", "access.desc": "Unterscheide, was du heute herunterladen kannst, von dem, was eine Anfrage erfordert, nur Benchmark, teilweise oder noch ungeprüft ist – bevor du Experimente planst.",
     "maintain.title": "Ressource hinzufügen", "maintain.desc": "Fehlt etwas? Schlage es in wenigen Schritten vor – öffne ein kurzes Issue, und es fließt in den Katalog ein.", "maintain.add": "Ressource hinzufügen",
     "maintain.contributing": "Beitragsleitfaden", "maintain.schema": "Ressourcenschema", "maintain.status": "Status-Richtlinie", "maintain.workflow": "Wartungsablauf",
@@ -143,11 +143,11 @@ const I18N = {
     skip: "カタログへスキップ",
     "nav.catalog": "カタログ", "nav.lanes": "研究分野", "nav.access": "アクセス", "nav.readme": "README", "nav.milestones": "マイルストーン",
     "milestones.title": "マイルストーン", "milestones.desc": "エゴセントリック AI を形づくった画期的な研究——分野の起源から最前線まで。",
-    "hero.lead": "一人称視点 AI の厳選マップ——エゴセントリック視覚、身体性 AI とロボティクス、ビデオ言語、長文脈記憶、AR/VR、手と物体の相互作用を支えるデータセット・ベンチマーク・モデル・ツールを収録。",
     "btn.github": "GitHub リポジトリ", "btn.hf": "Hugging Face ミラー", "btn.browse": "カタログを見る",
     "stat.resources": "エゴセントリック資源", "stat.datasets": "データセット", "stat.benchmarks": "ベンチマーク", "stat.models": "モデル", "stat.toolkits": "ツールキット",
     "proof.1": "データセット・ベンチマーク・モデル・ツール", "proof.2": "タスク・状態・日付で絞り込み", "proof.3": "オープンアクセス、MIT ライセンス",
-    "media.caption": "一人称視点 AI の全体像",
     "catalog.title": "カタログエクスプローラー", "catalog.desc": "名前・タスク・モダリティ・状態・種類・研究分野で検索。", "catalog.openyaml": "YAML を開く",
     "filter.search": "検索", "filter.kind": "種類", "filter.status": "状態", "filter.lane": "分野", "filter.reset": "リセット",
     "filter.allkinds": "すべての種類", "filter.allstatuses": "すべての状態", "filter.alllanes": "すべての分野",
@@ -156,7 +156,7 @@ const I18N = {
     loading: "カタログを読み込み中…", "note.default": "新しい順",
     "count.matching": "{n} 件の該当資源", "note.showing": "{b} 件中、最新 {a} 件を表示", "note.showingall": "{a} 件をすべて表示",
     "empty.title": "条件に一致する資源がありません。", "empty.hint": "検索範囲を広げる、分野を外す、またはフィルターをリセットしてください。",
-    "lanes.title": "研究分野", "lanes.desc": "一人称データの主な使い方への 6 つの入り口。", "lanes.taxonomy": "分類体系",
     "access.title": "アクセス状況", "access.desc": "実験を計画する前に、今すぐダウンロードできるものと、申請が必要・ベンチマークのみ・一部公開・未検証のものを見分けましょう。",
     "maintain.title": "資源を追加", "maintain.desc": "見つからない資源は？数ステップで提案できます——短い issue を開けばカタログに反映されます。", "maintain.add": "資源を追加",
     "maintain.contributing": "貢献ガイド", "maintain.schema": "資源スキーマ", "maintain.status": "状態ポリシー", "maintain.workflow": "メンテナンス手順",
@@ -169,11 +169,11 @@ const I18N = {
     skip: "카탈로그로 건너뛰기",
     "nav.catalog": "카탈로그", "nav.lanes": "연구 분야", "nav.access": "접근성", "nav.readme": "README", "nav.milestones": "이정표",
     "milestones.title": "이정표", "milestones.desc": "자기중심 AI를 형성한 획기적 연구 — 분야의 기원부터 현재 최전선까지.",
-    "hero.lead": "1인칭 AI의 엄선된 지도 — 자기중심 비전, 체화 AI와 로보틱스, 비디오-언어, 장문맥 기억, AR/VR, 손-물체 상호작용을 뒷받침하는 데이터셋·벤치마크·모델·도구를 담았습니다.",
     "btn.github": "GitHub 저장소", "btn.hf": "Hugging Face 미러", "btn.browse": "카탈로그 보기",
     "stat.resources": "자기중심 자원", "stat.datasets": "데이터셋", "stat.benchmarks": "벤치마크", "stat.models": "모델", "stat.toolkits": "툴킷",
     "proof.1": "데이터셋·벤치마크·모델·도구", "proof.2": "작업·상태·날짜로 필터링", "proof.3": "오픈 액세스, MIT 라이선스",
-    "media.caption": "1인칭 AI 한눈에",
     "catalog.title": "카탈로그 탐색기", "catalog.desc": "이름·작업·모달리티·상태·종류·연구 분야로 검색하세요.", "catalog.openyaml": "YAML 열기",
     "filter.search": "검색", "filter.kind": "종류", "filter.status": "상태", "filter.lane": "분야", "filter.reset": "초기화",
     "filter.allkinds": "모든 종류", "filter.allstatuses": "모든 상태", "filter.alllanes": "모든 분야",
@@ -182,7 +182,7 @@ const I18N = {
     loading: "카탈로그 불러오는 중...", "note.default": "최신순",
     "count.matching": "일치하는 자원 {n}개", "note.showing": "전체 {b}개 중 최신 {a}개 표시", "note.showingall": "전체 {a}개 표시",
     "empty.title": "해당 필터에 맞는 자원이 없습니다.", "empty.hint": "검색 범위를 넓히거나 분야를 제거하거나 필터를 초기화하세요.",
-    "lanes.title": "연구 분야", "lanes.desc": "1인칭 데이터를 활용하는 주요 방식으로 가는 여섯 가지 진입점.", "lanes.taxonomy": "분류 체계",
     "access.title": "접근성 현황", "access.desc": "실험을 계획하기 전에, 오늘 바로 받을 수 있는 것과 신청이 필요하거나 벤치마크 전용·일부 공개·미검증인 것을 구분하세요.",
     "maintain.title": "자원 추가", "maintain.desc": "빠진 자원이 있나요? 몇 단계로 제안하세요 — 짧은 이슈를 열면 카탈로그에 반영됩니다.", "maintain.add": "자원 추가",
     "maintain.contributing": "기여 가이드", "maintain.schema": "자원 스키마", "maintain.status": "상태 정책", "maintain.workflow": "유지보수 절차",
@@ -195,11 +195,11 @@ const I18N = {
     skip: "Ir para o catálogo",
     "nav.catalog": "Catálogo", "nav.lanes": "Áreas", "nav.access": "Acesso", "nav.readme": "README", "nav.milestones": "Marcos",
     "milestones.title": "Marcos", "milestones.desc": "Os trabalhos marcantes que moldaram a IA egocêntrica — das origens do campo à sua fronteira atual.",
-    "hero.lead": "Um mapa curado da IA em primeira pessoa: os conjuntos de dados, benchmarks, modelos e ferramentas por trás da visão egocêntrica, da IA incorporada e da robótica, da aprendizagem visão-linguagem, da memória de longo contexto, da RA/RV e da interação mão-objeto.",
     "btn.github": "Repositório GitHub", "btn.hf": "Espelho Hugging Face", "btn.browse": "Explorar catálogo",
     "stat.resources": "recursos egocêntricos", "stat.datasets": "conjuntos de dados", "stat.benchmarks": "benchmarks", "stat.models": "modelos", "stat.toolkits": "ferramentas",
     "proof.1": "Dados, benchmarks, modelos e ferramentas", "proof.2": "Filtre por tarefa, estado e data", "proof.3": "Acesso aberto, licença MIT",
-    "media.caption": "A IA em primeira pessoa, mapeada",
     "catalog.title": "Explorador do catálogo", "catalog.desc": "Pesquise por nome, tarefa, modalidade, estado, tipo ou área de pesquisa.", "catalog.openyaml": "Abrir YAML",
     "filter.search": "Pesquisar", "filter.kind": "Tipo", "filter.status": "Estado", "filter.lane": "Área", "filter.reset": "Redefinir",
     "filter.allkinds": "Todos os tipos", "filter.allstatuses": "Todos os estados", "filter.alllanes": "Todas as áreas",
@@ -208,7 +208,7 @@ const I18N = {
     loading: "Carregando catálogo...", "note.default": "Mais recentes primeiro",
     "count.matching": "{n} recursos correspondentes", "note.showing": "Mostrando os {a} mais recentes de {b}", "note.showingall": "Mostrando todos os {a}",
     "empty.title": "Nenhum recurso corresponde a esses filtros.", "empty.hint": "Tente uma busca mais ampla, remova uma área ou redefina os filtros.",
-    "lanes.title": "Áreas de pesquisa", "lanes.desc": "Seis portas de entrada para as principais formas de usar dados em primeira pessoa.", "lanes.taxonomy": "Taxonomia",
     "access.title": "Realidade de acesso", "access.desc": "Veja o que você pode baixar hoje versus o que exige solicitação, é só benchmark, é parcial ou ainda não verificado — antes de planejar experimentos.",
     "maintain.title": "Adicionar um recurso", "maintain.desc": "Falta algo? Sugira em poucos passos — abra uma breve issue e ele entra no catálogo.", "maintain.add": "Adicionar recurso",
     "maintain.contributing": "Guia de contribuição", "maintain.schema": "Esquema de recursos", "maintain.status": "Política de estados", "maintain.workflow": "Fluxo de manutenção",
@@ -297,10 +297,10 @@ const els = {
   clear: document.querySelector("#clear-filters"),
   summary: document.querySelector("#catalog-summary"),
   lanes: document.querySelector("#lane-grid"),
-  milestones: document.querySelector("#milestone-grid"),
   statuses: document.querySelector("#status-list"),
   empty: document.querySelector("#empty-state"),
-  langBar: document.querySelector("#lang-bar")
 };
 function titleize(value) {
@@ -538,6 +538,29 @@ function renderStatuses() {
   });
 }
 function bindFilters() {
   els.filters.addEventListener("input", () => {
     state.filters.search = els.search.value;
@@ -559,34 +582,6 @@ function bindFilters() {
   });
 }
-function renderMilestones() {
-  if (!els.milestones) return;
-  const items = state.data.milestones || [];
-  els.milestones.replaceChildren();
-  items.forEach((m) => {
-    const li = document.createElement("li");
-    li.className = `milestone kind-${escapeHtml(m.kind)}`;
-    const image = m.image
-      ? `<img class="milestone-thumb" src="${escapeHtml(m.image)}" alt="" loading="lazy" decoding="async">`
-      : "";
-    li.innerHTML = `
-      <span class="milestone-dot" aria-hidden="true"></span>
-      <div class="milestone-content">
-        ${image}
-        <div class="milestone-copy">
-          <div class="milestone-meta">
-            <span class="milestone-date">${escapeHtml(m.date)}</span>
-            <span class="chip">${escapeHtml(titleize(m.kind))}</span>
-          </div>
-          <a class="milestone-name" href="${escapeHtml(m.url)}">${escapeHtml(m.name)}</a>
-          <p class="milestone-note">${escapeHtml(m.note)}</p>
-        </div>
-      </div>
-    `;
-    els.milestones.appendChild(li);
-  });
-}
 async function init() {
   applyStaticI18n();
   buildLangBar();
@@ -594,10 +589,10 @@ async function init() {
   state.data = await response.json();
   renderStats();
   renderSummary();
-  renderMilestones();
   renderFilters();
   renderLanes();
   renderStatuses();
   applyFiltersToForm();
   bindFilters();
   renderRows();

     skip: "Skip to catalog",
     "nav.catalog": "Catalog", "nav.lanes": "Lanes", "nav.access": "Access", "nav.readme": "README", "nav.milestones": "Milestones",
     "milestones.title": "Milestones", "milestones.desc": "The landmark works that shaped egocentric AI — from the field's origins to its current frontier.",
+    "hero.lead": "A curated map of egocentric AI — the datasets, benchmarks, models, and tools behind egocentric vision, embodied AI and robotics, video-language, long-context memory, AR/VR, and hand-object interaction.",
     "btn.github": "GitHub Repo", "btn.hf": "Hugging Face Mirror", "btn.browse": "Browse Catalog",
     "stat.resources": "egocentric resources", "stat.datasets": "datasets", "stat.benchmarks": "benchmarks", "stat.models": "models", "stat.toolkits": "toolkits",
     "proof.1": "Datasets, benchmarks, models & tools", "proof.2": "Filter by task, status, and date", "proof.3": "Open access, MIT licensed",
+    "media.caption": "Egocentric AI, mapped",
     "catalog.title": "Catalog Explorer", "catalog.desc": "Search by name, task, modality, status, kind, or research lane.", "catalog.openyaml": "Open YAML",
     "filter.search": "Search", "filter.kind": "Kind", "filter.status": "Status", "filter.lane": "Lane", "filter.reset": "Reset",
     "filter.allkinds": "All kinds", "filter.allstatuses": "All statuses", "filter.alllanes": "All lanes",
     loading: "Loading catalog...", "note.default": "Newest matches first",
     "count.matching": "{n} matching resources", "note.showing": "Showing newest {a} of {b}", "note.showingall": "Showing all {a}",
     "empty.title": "No resources match those filters.", "empty.hint": "Try a broader search, remove a lane, or reset the filters.",
+    "lanes.title": "Research Lanes", "lanes.desc": "Six entry points for the main ways people use egocentric data.", "lanes.taxonomy": "Taxonomy",
     "access.title": "Access Reality", "access.desc": "See what you can download today versus what needs a request, is benchmark-only, partial, or still unverified — before you plan experiments.",
     "maintain.title": "Add a Resource", "maintain.desc": "Found something we're missing? Suggest it in a few steps — open a short issue and it flows into the catalog.", "maintain.add": "Add Resource",
     "maintain.contributing": "Contributing guide", "maintain.schema": "Resource schema", "maintain.status": "Status policy", "maintain.workflow": "Maintenance workflow",
     skip: "跳到目录",
     "nav.catalog": "目录", "nav.lanes": "方向", "nav.access": "可获取性", "nav.readme": "README", "nav.milestones": "里程碑",
     "milestones.title": "里程碑", "milestones.desc": "塑造自我中心 AI 的里程碑工作——从领域起源到当前前沿。",
+    "hero.lead": "自我中心 AI 的精选地图——汇集自我中心视觉、具身智能与机器人、视频语言、长上下文记忆、AR/VR 以及手物交互背后的数据集、基准、模型与工具。",
     "btn.github": "GitHub 仓库", "btn.hf": "Hugging Face 镜像", "btn.browse": "浏览目录",
     "stat.resources": "自我中心资源", "stat.datasets": "数据集", "stat.benchmarks": "基准", "stat.models": "模型", "stat.toolkits": "工具包",
     "proof.1": "数据集、基准、模型与工具", "proof.2": "按任务、状态与日期筛选", "proof.3": "开放获取，MIT 许可",
+    "media.caption": "自我中心 AI 全景",
     "catalog.title": "目录浏览器", "catalog.desc": "按名称、任务、模态、状态、类型或研究方向搜索。", "catalog.openyaml": "打开 YAML",
     "filter.search": "搜索", "filter.kind": "类型", "filter.status": "状态", "filter.lane": "方向", "filter.reset": "重置",
     "filter.allkinds": "全部类型", "filter.allstatuses": "全部状态", "filter.alllanes": "全部方向",
     loading: "正在加载目录……", "note.default": "最新优先",
     "count.matching": "{n} 个匹配资源", "note.showing": "显示最新 {a} / 共 {b}", "note.showingall": "共显示 {a} 个",
     "empty.title": "没有资源符合这些筛选条件。", "empty.hint": "尝试更宽泛的搜索、移除某个方向，或重置筛选。",
+    "lanes.title": "研究方向", "lanes.desc": "六个入口，覆盖使用自我中心数据的主要方式。", "lanes.taxonomy": "分类体系",
     "access.title": "可获取性一览", "access.desc": "在规划实验前，先看清哪些今天即可下载，哪些需要申请、仅为基准、部分公开，或尚待核实。",
     "maintain.title": "添加资源", "maintain.desc": "发现我们遗漏的资源？只需几步即可提交——开一个简短的 issue，它就会进入目录。", "maintain.add": "添加资源",
     "maintain.contributing": "贡献指南", "maintain.schema": "资源结构", "maintain.status": "状态政策", "maintain.workflow": "维护流程",
     skip: "Saltar al catálogo",
     "nav.catalog": "Catálogo", "nav.lanes": "Áreas", "nav.access": "Acceso", "nav.readme": "README", "nav.milestones": "Hitos",
     "milestones.title": "Hitos", "milestones.desc": "Los trabajos de referencia que dieron forma a la IA egocéntrica, desde sus orígenes hasta la frontera actual.",
+    "hero.lead": "Un mapa curado de la IA egocéntrica: los conjuntos de datos, benchmarks, modelos y herramientas tras la visión egocéntrica, la IA encarnada y la robótica, el aprendizaje visión-lenguaje, la memoria de largo contexto, la RA/RV y la interacción mano-objeto.",
     "btn.github": "Repositorio GitHub", "btn.hf": "Espejo en Hugging Face", "btn.browse": "Explorar catálogo",
     "stat.resources": "recursos egocéntricos", "stat.datasets": "conjuntos de datos", "stat.benchmarks": "benchmarks", "stat.models": "modelos", "stat.toolkits": "herramientas",
     "proof.1": "Datos, benchmarks, modelos y herramientas", "proof.2": "Filtra por tarea, estado y fecha", "proof.3": "Acceso abierto, licencia MIT",
+    "media.caption": "La IA egocéntrica, mapeada",
     "catalog.title": "Explorador del catálogo", "catalog.desc": "Busca por nombre, tarea, modalidad, estado, tipo o área de investigación.", "catalog.openyaml": "Abrir YAML",
     "filter.search": "Buscar", "filter.kind": "Tipo", "filter.status": "Estado", "filter.lane": "Área", "filter.reset": "Restablecer",
     "filter.allkinds": "Todos los tipos", "filter.allstatuses": "Todos los estados", "filter.alllanes": "Todas las áreas",
     loading: "Cargando catálogo...", "note.default": "Primero los más recientes",
     "count.matching": "{n} recursos coincidentes", "note.showing": "Mostrando los {a} más recientes de {b}", "note.showingall": "Mostrando los {a}",
     "empty.title": "Ningún recurso coincide con esos filtros.", "empty.hint": "Prueba una búsqueda más amplia, quita un área o restablece los filtros.",
+    "lanes.title": "Áreas de investigación", "lanes.desc": "Seis puntos de entrada a las formas principales de usar datos egocéntricos.", "lanes.taxonomy": "Taxonomía",
     "access.title": "Realidad de acceso", "access.desc": "Distingue lo que puedes descargar hoy de lo que requiere solicitud, es solo benchmark, es parcial o aún no está verificado, antes de planificar experimentos.",
     "maintain.title": "Añadir un recurso", "maintain.desc": "¿Falta algo? Propónlo en unos pocos pasos: abre una breve incidencia y entrará en el catálogo.", "maintain.add": "Añadir recurso",
     "maintain.contributing": "Guía de contribución", "maintain.schema": "Esquema de recursos", "maintain.status": "Política de estados", "maintain.workflow": "Flujo de mantenimiento",
     skip: "Aller au catalogue",
     "nav.catalog": "Catalogue", "nav.lanes": "Axes", "nav.access": "Accès", "nav.readme": "README", "nav.milestones": "Jalons",
     "milestones.title": "Jalons", "milestones.desc": "Les travaux marquants qui ont façonné l'IA égocentrique, des origines du domaine à sa frontière actuelle.",
+    "hero.lead": "Une carte sélective de l'IA égocentrique : les jeux de données, benchmarks, modèles et outils derrière la vision égocentrique, l'IA incarnée et la robotique, l'apprentissage vision-langage, la mémoire à long contexte, la RA/RV et l'interaction main-objet.",
     "btn.github": "Dépôt GitHub", "btn.hf": "Miroir Hugging Face", "btn.browse": "Parcourir le catalogue",
     "stat.resources": "ressources égocentriques", "stat.datasets": "jeux de données", "stat.benchmarks": "benchmarks", "stat.models": "modèles", "stat.toolkits": "outils",
     "proof.1": "Données, benchmarks, modèles et outils", "proof.2": "Filtrer par tâche, statut et date", "proof.3": "Accès libre, licence MIT",
+    "media.caption": "L'IA égocentrique, cartographiée",
     "catalog.title": "Explorateur du catalogue", "catalog.desc": "Recherchez par nom, tâche, modalité, statut, type ou axe de recherche.", "catalog.openyaml": "Ouvrir le YAML",
     "filter.search": "Rechercher", "filter.kind": "Type", "filter.status": "Statut", "filter.lane": "Axe", "filter.reset": "Réinitialiser",
     "filter.allkinds": "Tous les types", "filter.allstatuses": "Tous les statuts", "filter.alllanes": "Tous les axes",
     loading: "Chargement du catalogue...", "note.default": "Les plus récents d'abord",
     "count.matching": "{n} ressources correspondantes", "note.showing": "Affichage des {a} plus récentes sur {b}", "note.showingall": "Affichage des {a}",
     "empty.title": "Aucune ressource ne correspond à ces filtres.", "empty.hint": "Essayez une recherche plus large, retirez un axe ou réinitialisez les filtres.",
+    "lanes.title": "Axes de recherche", "lanes.desc": "Six points d'entrée vers les principales façons d'utiliser les données égocentriques.", "lanes.taxonomy": "Taxonomie",
     "access.title": "Réalité de l'accès", "access.desc": "Distinguez ce que vous pouvez télécharger aujourd'hui de ce qui nécessite une demande, est réservé aux benchmarks, partiel ou non vérifié, avant de planifier vos expériences.",
     "maintain.title": "Ajouter une ressource", "maintain.desc": "Une ressource manquante ? Proposez-la en quelques étapes : ouvrez un court ticket et elle rejoindra le catalogue.", "maintain.add": "Ajouter une ressource",
     "maintain.contributing": "Guide de contribution", "maintain.schema": "Schéma des ressources", "maintain.status": "Politique des statuts", "maintain.workflow": "Flux de maintenance",
     skip: "Zum Katalog springen",
     "nav.catalog": "Katalog", "nav.lanes": "Bereiche", "nav.access": "Zugang", "nav.readme": "README", "nav.milestones": "Meilensteine",
     "milestones.title": "Meilensteine", "milestones.desc": "Die wegweisenden Arbeiten, die egozentrische KI geprägt haben – von den Ursprüngen bis zur aktuellen Front.",
+    "hero.lead": "Eine kuratierte Karte egozentrischer KI – die Datensätze, Benchmarks, Modelle und Werkzeuge hinter egozentrischem Sehen, verkörperter KI und Robotik, Video-Sprache, Langzeitgedächtnis, AR/VR und Hand-Objekt-Interaktion.",
     "btn.github": "GitHub-Repo", "btn.hf": "Hugging-Face-Spiegel", "btn.browse": "Katalog durchsuchen",
     "stat.resources": "egozentrische Ressourcen", "stat.datasets": "Datensätze", "stat.benchmarks": "Benchmarks", "stat.models": "Modelle", "stat.toolkits": "Toolkits",
     "proof.1": "Datensätze, Benchmarks, Modelle & Werkzeuge", "proof.2": "Nach Aufgabe, Status und Datum filtern", "proof.3": "Offen zugänglich, MIT-Lizenz",
+    "media.caption": "Egozentrische KI, kartiert",
     "catalog.title": "Katalog-Explorer", "catalog.desc": "Suche nach Name, Aufgabe, Modalität, Status, Art oder Forschungsbereich.", "catalog.openyaml": "YAML öffnen",
     "filter.search": "Suche", "filter.kind": "Art", "filter.status": "Status", "filter.lane": "Bereich", "filter.reset": "Zurücksetzen",
     "filter.allkinds": "Alle Arten", "filter.allstatuses": "Alle Status", "filter.alllanes": "Alle Bereiche",
     loading: "Katalog wird geladen...", "note.default": "Neueste zuerst",
     "count.matching": "{n} passende Ressourcen", "note.showing": "Zeige die neuesten {a} von {b}", "note.showingall": "Zeige alle {a}",
     "empty.title": "Keine Ressourcen passen zu diesen Filtern.", "empty.hint": "Versuche eine breitere Suche, entferne einen Bereich oder setze die Filter zurück.",
+    "lanes.title": "Forschungsbereiche", "lanes.desc": "Sechs Einstiegspunkte für die wichtigsten Wege, egozentrische Daten zu nutzen.", "lanes.taxonomy": "Taxonomie",
     "access.title": "Zugangslage", "access.desc": "Unterscheide, was du heute herunterladen kannst, von dem, was eine Anfrage erfordert, nur Benchmark, teilweise oder noch ungeprüft ist – bevor du Experimente planst.",
     "maintain.title": "Ressource hinzufügen", "maintain.desc": "Fehlt etwas? Schlage es in wenigen Schritten vor – öffne ein kurzes Issue, und es fließt in den Katalog ein.", "maintain.add": "Ressource hinzufügen",
     "maintain.contributing": "Beitragsleitfaden", "maintain.schema": "Ressourcenschema", "maintain.status": "Status-Richtlinie", "maintain.workflow": "Wartungsablauf",
     skip: "カタログへスキップ",
     "nav.catalog": "カタログ", "nav.lanes": "研究分野", "nav.access": "アクセス", "nav.readme": "README", "nav.milestones": "マイルストーン",
     "milestones.title": "マイルストーン", "milestones.desc": "エゴセントリック AI を形づくった画期的な研究——分野の起源から最前線まで。",
+    "hero.lead": "エゴセントリック AI の厳選マップ——エゴセントリック視覚、身体性 AI とロボティクス、ビデオ言語、長文脈記憶、AR/VR、手と物体の相互作用を支えるデータセット・ベンチマーク・モデル・ツールを収録。",
     "btn.github": "GitHub リポジトリ", "btn.hf": "Hugging Face ミラー", "btn.browse": "カタログを見る",
     "stat.resources": "エゴセントリック資源", "stat.datasets": "データセット", "stat.benchmarks": "ベンチマーク", "stat.models": "モデル", "stat.toolkits": "ツールキット",
     "proof.1": "データセット・ベンチマーク・モデル・ツール", "proof.2": "タスク・状態・日付で絞り込み", "proof.3": "オープンアクセス、MIT ライセンス",
+    "media.caption": "エゴセントリック AI の全体像",
     "catalog.title": "カタログエクスプローラー", "catalog.desc": "名前・タスク・モダリティ・状態・種類・研究分野で検索。", "catalog.openyaml": "YAML を開く",
     "filter.search": "検索", "filter.kind": "種類", "filter.status": "状態", "filter.lane": "分野", "filter.reset": "リセット",
     "filter.allkinds": "すべての種類", "filter.allstatuses": "すべての状態", "filter.alllanes": "すべての分野",
     loading: "カタログを読み込み中…", "note.default": "新しい順",
     "count.matching": "{n} 件の該当資源", "note.showing": "{b} 件中、最新 {a} 件を表示", "note.showingall": "{a} 件をすべて表示",
     "empty.title": "条件に一致する資源がありません。", "empty.hint": "検索範囲を広げる、分野を外す、またはフィルターをリセットしてください。",
+    "lanes.title": "研究分野", "lanes.desc": "エゴセントリックデータの主な使い方への 6 つの入り口。", "lanes.taxonomy": "分類体系",
     "access.title": "アクセス状況", "access.desc": "実験を計画する前に、今すぐダウンロードできるものと、申請が必要・ベンチマークのみ・一部公開・未検証のものを見分けましょう。",
     "maintain.title": "資源を追加", "maintain.desc": "見つからない資源は？数ステップで提案できます——短い issue を開けばカタログに反映されます。", "maintain.add": "資源を追加",
     "maintain.contributing": "貢献ガイド", "maintain.schema": "資源スキーマ", "maintain.status": "状態ポリシー", "maintain.workflow": "メンテナンス手順",
     skip: "카탈로그로 건너뛰기",
     "nav.catalog": "카탈로그", "nav.lanes": "연구 분야", "nav.access": "접근성", "nav.readme": "README", "nav.milestones": "이정표",
     "milestones.title": "이정표", "milestones.desc": "자기중심 AI를 형성한 획기적 연구 — 분야의 기원부터 현재 최전선까지.",
+    "hero.lead": "자기중심 AI의 엄선된 지도 — 자기중심 비전, 체화 AI와 로보틱스, 비디오-언어, 장문맥 기억, AR/VR, 손-물체 상호작용을 뒷받침하는 데이터셋·벤치마크·모델·도구를 담았습니다.",
     "btn.github": "GitHub 저장소", "btn.hf": "Hugging Face 미러", "btn.browse": "카탈로그 보기",
     "stat.resources": "자기중심 자원", "stat.datasets": "데이터셋", "stat.benchmarks": "벤치마크", "stat.models": "모델", "stat.toolkits": "툴킷",
     "proof.1": "데이터셋·벤치마크·모델·도구", "proof.2": "작업·상태·날짜로 필터링", "proof.3": "오픈 액세스, MIT 라이선스",
+    "media.caption": "자기중심 AI 한눈에",
     "catalog.title": "카탈로그 탐색기", "catalog.desc": "이름·작업·모달리티·상태·종류·연구 분야로 검색하세요.", "catalog.openyaml": "YAML 열기",
     "filter.search": "검색", "filter.kind": "종류", "filter.status": "상태", "filter.lane": "분야", "filter.reset": "초기화",
     "filter.allkinds": "모든 종류", "filter.allstatuses": "모든 상태", "filter.alllanes": "모든 분야",
     loading: "카탈로그 불러오는 중...", "note.default": "최신순",
     "count.matching": "일치하는 자원 {n}개", "note.showing": "전체 {b}개 중 최신 {a}개 표시", "note.showingall": "전체 {a}개 표시",
     "empty.title": "해당 필터에 맞는 자원이 없습니다.", "empty.hint": "검색 범위를 넓히거나 분야를 제거하거나 필터를 초기화하세요.",
+    "lanes.title": "연구 분야", "lanes.desc": "자기중심 데이터를 활용하는 주요 방식으로 가는 여섯 가지 진입점.", "lanes.taxonomy": "분류 체계",
     "access.title": "접근성 현황", "access.desc": "실험을 계획하기 전에, 오늘 바로 받을 수 있는 것과 신청이 필요하거나 벤치마크 전용·일부 공개·미검증인 것을 구분하세요.",
     "maintain.title": "자원 추가", "maintain.desc": "빠진 자원이 있나요? 몇 단계로 제안하세요 — 짧은 이슈를 열면 카탈로그에 반영됩니다.", "maintain.add": "자원 추가",
     "maintain.contributing": "기여 가이드", "maintain.schema": "자원 스키마", "maintain.status": "상태 정책", "maintain.workflow": "유지보수 절차",
     skip: "Ir para o catálogo",
     "nav.catalog": "Catálogo", "nav.lanes": "Áreas", "nav.access": "Acesso", "nav.readme": "README", "nav.milestones": "Marcos",
     "milestones.title": "Marcos", "milestones.desc": "Os trabalhos marcantes que moldaram a IA egocêntrica — das origens do campo à sua fronteira atual.",
+    "hero.lead": "Um mapa curado da IA egocêntrica: os conjuntos de dados, benchmarks, modelos e ferramentas por trás da visão egocêntrica, da IA incorporada e da robótica, da aprendizagem visão-linguagem, da memória de longo contexto, da RA/RV e da interação mão-objeto.",
     "btn.github": "Repositório GitHub", "btn.hf": "Espelho Hugging Face", "btn.browse": "Explorar catálogo",
     "stat.resources": "recursos egocêntricos", "stat.datasets": "conjuntos de dados", "stat.benchmarks": "benchmarks", "stat.models": "modelos", "stat.toolkits": "ferramentas",
     "proof.1": "Dados, benchmarks, modelos e ferramentas", "proof.2": "Filtre por tarefa, estado e data", "proof.3": "Acesso aberto, licença MIT",
+    "media.caption": "A IA egocêntrica, mapeada",
     "catalog.title": "Explorador do catálogo", "catalog.desc": "Pesquise por nome, tarefa, modalidade, estado, tipo ou área de pesquisa.", "catalog.openyaml": "Abrir YAML",
     "filter.search": "Pesquisar", "filter.kind": "Tipo", "filter.status": "Estado", "filter.lane": "Área", "filter.reset": "Redefinir",
     "filter.allkinds": "Todos os tipos", "filter.allstatuses": "Todos os estados", "filter.alllanes": "Todas as áreas",
     loading: "Carregando catálogo...", "note.default": "Mais recentes primeiro",
     "count.matching": "{n} recursos correspondentes", "note.showing": "Mostrando os {a} mais recentes de {b}", "note.showingall": "Mostrando todos os {a}",
     "empty.title": "Nenhum recurso corresponde a esses filtros.", "empty.hint": "Tente uma busca mais ampla, remova uma área ou redefina os filtros.",
+    "lanes.title": "Áreas de pesquisa", "lanes.desc": "Seis portas de entrada para as principais formas de usar dados egocêntricos.", "lanes.taxonomy": "Taxonomia",
     "access.title": "Realidade de acesso", "access.desc": "Veja o que você pode baixar hoje versus o que exige solicitação, é só benchmark, é parcial ou ainda não verificado — antes de planejar experimentos.",
     "maintain.title": "Adicionar um recurso", "maintain.desc": "Falta algo? Sugira em poucos passos — abra uma breve issue e ele entra no catálogo.", "maintain.add": "Adicionar recurso",
     "maintain.contributing": "Guia de contribuição", "maintain.schema": "Esquema de recursos", "maintain.status": "Política de estados", "maintain.workflow": "Fluxo de manutenção",
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   summary: document.querySelector("#catalog-summary"),
   lanes: document.querySelector("#lane-grid"),
   statuses: document.querySelector("#status-list"),
   empty: document.querySelector("#empty-state"),
+  langBar: document.querySelector("#lang-bar"),
+  milestoneLinks: document.querySelector("#milestone-link-layer")
 };
 function titleize(value) {
   });
 }
+function renderMilestoneLinks() {
+  if (!els.milestoneLinks || !state.data.milestone_layout) return;
+  const layout = state.data.milestone_layout;
+  const width = Number(layout.width) || 1280;
+  const height = Number(layout.height) || 2322;
+  els.milestoneLinks.replaceChildren();
+  (layout.cells || []).forEach((cell) => {
+    const link = document.createElement("a");
+    link.className = "milestone-cell-link";
+    link.href = cell.url;
+    link.target = "_blank";
+    link.rel = "noopener noreferrer";
+    link.title = `${cell.name} (${cell.date})`;
+    link.setAttribute("aria-label", `${cell.name} (${cell.date})`);
+    link.style.left = `${(Number(cell.x) / width) * 100}%`;
+    link.style.top = `${(Number(cell.y) / height) * 100}%`;
+    link.style.width = `${(Number(cell.width) / width) * 100}%`;
+    link.style.height = `${(Number(cell.height) / height) * 100}%`;
+    els.milestoneLinks.appendChild(link);
+  });
+}
 function bindFilters() {
   els.filters.addEventListener("input", () => {
     state.filters.search = els.search.value;
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 async function init() {
   applyStaticI18n();
   buildLangBar();
   state.data = await response.json();
   renderStats();
   renderSummary();
   renderFilters();
   renderLanes();
   renderStatuses();
+  renderMilestoneLinks();
   applyFiltersToForm();
   bindFilters();
   renderRows();

assets/README.md CHANGED Viewed

@@ -33,8 +33,8 @@ The README and GitHub Pages site embed high-resolution PNG exports of the SVG fi
 - `awesome-egocentric-atlas-map.png` — `3840 x 2190`
 - `awesome-egocentric-reader-route.png` — `3840 x 1380`
-- `awesome-egocentric-timeline.png` — `3840 x 1560`
-- `awesome-egocentric-milestones.png` — `3840 x 11190`
 - `awesome-egocentric-task-matrix.png` — `3840 x 2220`
 - `awesome-egocentric-access-funnel.png` — `3840 x 1560`
@@ -77,5 +77,6 @@ Each SVG figure is plain, well-formed SVG and stays diffable. Most are fully sel
 - `umi.png` — handheld UMI gripper demonstrations for robot learning.
 - `egolife.png` — long-term smart-glasses memory and daily-life assistant reasoning.
 - `egovla.png` — human egocentric demonstrations transferring to robot policies.
 - `egoscale.png` — data-scaling curve for egocentric VLA pretraining.
 - `xperience-10m.png` — petascale multimodal egocentric world-model corpus.

 - `awesome-egocentric-atlas-map.png` — `3840 x 2190`
 - `awesome-egocentric-reader-route.png` — `3840 x 1380`
+- `awesome-egocentric-timeline.png` — `3840 x 1680`
+- `awesome-egocentric-milestones.png` — `3840 x 6966`
 - `awesome-egocentric-task-matrix.png` — `3840 x 2220`
 - `awesome-egocentric-access-funnel.png` — `3840 x 1560`
 - `umi.png` — handheld UMI gripper demonstrations for robot learning.
 - `egolife.png` — long-term smart-glasses memory and daily-life assistant reasoning.
 - `egovla.png` — human egocentric demonstrations transferring to robot policies.
+- `dreamdojo.png` — egocentric human videos distilled into latent-action robot world-model planning.
 - `egoscale.png` — data-scaling curve for egocentric VLA pretraining.
 - `xperience-10m.png` — petascale multimodal egocentric world-model corpus.

assets/awesome-egocentric-access-funnel.png CHANGED Viewed

Git LFS Details

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assets/awesome-egocentric-atlas-map.png CHANGED Viewed

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assets/awesome-egocentric-atlas-map.svg CHANGED Viewed

assets/awesome-egocentric-milestones.png CHANGED Viewed

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Pointer size: 133 Bytes
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SHA256: cec988be8b11425ddd83b59d2bac3e20a27afe1667e7b9d282b43369ee792491
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assets/awesome-egocentric-milestones.svg CHANGED Viewed

assets/awesome-egocentric-task-matrix.png CHANGED Viewed

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assets/awesome-egocentric-task-matrix.svg CHANGED Viewed

assets/awesome-egocentric-timeline.png CHANGED Viewed

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Pointer size: 132 Bytes
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Git LFS Details

SHA256: 897069e29211651218cff4f6c59c0a55850bab63d57644bf8f86632f0eae5747
Pointer size: 131 Bytes
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assets/awesome-egocentric-timeline.svg CHANGED Viewed

assets/milestones/dreamdojo.png ADDED Viewed

Git LFS Details

SHA256: d7cf511bb818036eb20b854a55879349fc1fc500042fb75ec0316659916b3166
Pointer size: 132 Bytes
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awesome-egocentric-atlas.csv CHANGED Viewed

@@ -1,6 +1,6 @@
 name,kind,released,venue,status,scope,year,url,paper,code,license,scale,tasks,modalities
 Xperience-10M,dataset,2026-03,Hugging Face,request,,2026,https://huggingface.co/datasets/ropedia-ai/xperience-10m,,,other,"10M experiences, 10K hours, six video streams, audio, stereo depth, camera pose, hand/body mocap, IMU, hierarchical language, about 1 PB total",embodied-ai; world-modeling; robot-learning; sensor-fusion; 3d-4d-understanding; imitation-learning,fisheye-video; stereo-video; audio; stereo-depth; camera-pose; hand-mocap; full-body-mocap; imu; hierarchical-language
-Xperience-10M Sample,dataset,2026,Hugging Face,open,,2026,https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample,,,cc-by-nc-4.0,Public sample episode; Hugging Face viewer reports 6 rows,sample-data; loader-testing; visualization; task-suite-prototyping,video; hdf5-annotations
 Ego4D,dataset,2021-10,CVPR 2022,request,,2021,https://ego4d-data.org/,https://arxiv.org/abs/2110.07058,https://github.com/facebookresearch/Ego4d,Ego4D License Agreement,"3,670+ hours, 900+ camera wearers, 74 locations, 9 countries",episodic-memory; nlq; moment-query; hand-object; forecasting; social; audio-visual,video; audio; gaze; stereo; 3d; narrations
 Ego-Exo4D,dataset,2023-11,CVPR 2024,request,,2023,https://ego-exo4d-data.org/,https://arxiv.org/abs/2311.18259,https://github.com/facebookresearch/projectaria_tools,Ego-Exo4D License Agreement,"1,286 hours, 740 participants, synchronized ego/exo views",ego-exo; skilled-activity; cross-view; proficiency; pose,video; audio; gaze; imu; 3d-point-clouds; camera-poses; language
 EPIC-KITCHENS-100,dataset,2020-06,IJCV 2022,open,,2020,https://epic-kitchens.github.io/,https://arxiv.org/abs/2006.13256,https://github.com/epic-kitchens/epic-kitchens-100-annotations,CC BY-NC 4.0,"100 hours, 20M frames, 90K action segments, 45 kitchens",action-recognition; action-detection; anticipation; retrieval; domain-adaptation,video; audio; narrations; object-boxes
@@ -30,20 +30,20 @@ HOT3D,dataset,2024-06,CVPR 2025,open,,2024,https://facebookresearch.github.io/ho
 HOI4D,dataset,2022-03,CVPR 2022,open,,2022,https://hoi4d.github.io/,https://arxiv.org/abs/2203.01577,,CC BY-NC 4.0,"2.4M RGB-D egocentric frames, 4,000+ sequences, 800 objects",4d-hoi; pose-tracking; action-segmentation,rgbd; point-clouds; 3d-hand-pose; object-pose; segmentation
 H2O,dataset,2021-04,ICCV 2021,open,,2021,https://arxiv.org/abs/2104.11181,https://arxiv.org/abs/2104.11181,,not specified,,first-person-interaction-recognition; two-hand-pose; hand-object-pose,multiview-rgbd; 3d-hand-pose; object-pose; camera-pose; object-meshes; scene-point-clouds
 ARCTIC,dataset,2022-04,CVPR 2023,request,,2022,https://arctic.is.tue.mpg.de/,https://arxiv.org/abs/2204.13662,,,2.1M frames,bimanual-manipulation; reconstruction; interaction-field-estimation,video; hand-meshes; object-meshes; contact; articulated-objects
-EgoHands,dataset,2015,ICCV 2015,open,,2015,http://vision.soic.indiana.edu/projects/egohands/,,,not specified,"48 Google Glass videos, 4,800 annotated images",hand-detection; hand-segmentation,rgb; hand-masks
-FPHA,dataset,2017,CVPR 2018,open,,2017,https://guiggh.github.io/publications/first-person-hands/,,,not specified,"100K+ RGB-D frames, 45 action classes, 26 objects",hand-action-recognition; 3d-hand-pose,rgbd; 3d-hand-pose; object-pose
 EgoHOS,dataset,2022-08,ECCV 2022,open,,2022,https://github.com/owenzlz/EgoHOS,https://arxiv.org/abs/2208.03826,,MIT,"11,243 egocentric images",hand-object-segmentation; contact-understanding,rgb; hand-object-contact-masks
 Ego2Hands,dataset,2020-11,arXiv,open,,2020,https://arxiv.org/abs/2011.07252,https://arxiv.org/abs/2011.07252,,not specified,,two-hand-segmentation; hand-detection,rgb; synthetic-composited-masks
 Ego2HandsPose,dataset,2022-06,arXiv,open,,2022,https://arxiv.org/abs/2206.04927,https://arxiv.org/abs/2206.04927,,not specified,,two-hand-3d-pose,rgb; 3d-hand-pose
-EgoBody,dataset,2022,ECCV 2022,open,,2022,https://sanweiliti.github.io/egobody/egobody.html,,,not specified,,3d-human-pose; body-shape; motion,hololens2; rgb; depth; gaze; head-pose; hand-pose; body-pose
 GIMO,dataset,2022-04,ECCV 2022,open,,2022,https://github.com/y-zheng18/GIMO,https://arxiv.org/abs/2204.09443,,not specified,,gaze-informed-motion-prediction; human-motion; scene-context,egocentric-video; gaze; body-pose; scene-scans
 EgoHumans,benchmark,2023-05,ICCV 2023,partial,,2023,https://arxiv.org/abs/2305.16487,https://arxiv.org/abs/2305.16487,,,125K+ egocentric images,multi-human-tracking; 3d-pose; mesh-recovery,egocentric-video; multiview-video; 2d-pose; 3d-pose; mesh
 UnrealEgo,dataset,2022-08,ECCV 2022,open,,2022,https://4dqv.mpi-inf.mpg.de/UnrealEgo/,https://arxiv.org/abs/2208.01633,,not specified,,egocentric-3d-pose,synthetic-stereo; 3d-human-pose
-xR-EgoPose,dataset,2019,GitHub,open,,2019,https://github.com/facebookresearch/xR-EgoPose,,,custom,,xr-pose-estimation,synthetic-egocentric; 3d-human-pose
 EgoGTA / EgoPW-Scene,dataset,2022-12,arXiv,partial,,2022,https://arxiv.org/abs/2212.11684,https://arxiv.org/abs/2212.11684,,,,scene-aware-3d-human-pose; egocentric-depth; human-scene-interaction,synthetic-egocentric; scene-depth; human-pose
 EgoTracks,benchmark,2023-01,NeurIPS 2023,open,,2023,https://arxiv.org/abs/2301.03213,https://arxiv.org/abs/2301.03213,,,,long-term-object-tracking; redetection,
-TREK-150,benchmark,2021,project page,open,,2021,https://machinelearning.uniud.it/datasets/trek150/,,,,,single-object-tracking,
-Project Aria Datasets,collection,2022,project page,open,,2022,https://www.projectaria.com/datasets/,,,,,ar-perception; scene-understanding; wearable-sensing,aria; vrs; calibration; gaze; imu; mps
 Aria Digital Twin,dataset,2023-06,arXiv,open,,2023,https://www.projectaria.com/datasets/adt/,https://arxiv.org/abs/2306.06362,,CC BY-NC-SA 4.0,"200 sequences, 398 object instances",3d-machine-perception; object-tracking; scene-reconstruction,aria; rgb; monochrome; imu; 6dof; depth; segmentation; synthetic-rendering
 Aria Everyday Activities,dataset,2024-02,arXiv,open,,2024,https://www.projectaria.com/datasets/aea/,https://arxiv.org/abs/2402.13349,,CC BY-NC-SA 4.0,143 daily activity sequences across five indoor locations,everyday-activity; neural-scene-reconstruction; prompted-segmentation,aria; trajectory; point-cloud; gaze; speech
 Nymeria,dataset,2024-06,ECCV 2024,open,,2024,https://www.projectaria.com/datasets/nymeria/,https://arxiv.org/abs/2406.09905,,CC BY-NC-SA 4.0,"300 hours, 264 participants, 50 locations",motion-language; body-tracking; action-recognition,aria; eye-tracking; imu; body-motion; language; observer-view
@@ -87,9 +87,9 @@ DogCentric,dataset,2014,project page,open,,2014,https://robotics.ait.kyushu-u.ac
 KrishnaCam / OAK,dataset,2021-08,ICCV 2021,partial,,2021,https://oakdata.github.io/,https://arxiv.org/abs/2108.11005,,,"OAK: 80 snippets, about 17.5 hours, 105 object categories from KrishnaCam",continual-learning; object-detection; lifelogging,egocentric-video; object-boxes
 EgoK360,dataset,2020-10,ICIP 2020,partial,,2020,https://egok360.github.io/,https://arxiv.org/abs/2010.08055,,,,360-video; activity-recognition,first-person-360-video; action-labels
 EgoTraj,dataset,2026-05,arXiv,open,,2026,https://github.com/yehiahmad/EgoTraj,https://arxiv.org/abs/2605.19004,,custom,75 Meta Quest Pro navigation sequences,trajectory-prediction; navigation; assistive-systems,rgb; head-pose; gaze; scene-labels
-VISOR,benchmark,2022,NeurIPS 2022,open,,2022,https://epic-kitchens.github.io/VISOR/,,,cc-by-nc-4.0,,hand-segmentation; active-object-segmentation; relations,
-EPIC-Sounds,benchmark,2023,ICASSP 2023,open,,2023,https://epic-kitchens.github.io/epic-sounds/,,,,,audio-event-recognition,
-EPIC-Fields,benchmark,2023,NeurIPS 2023,open,,2023,https://epic-kitchens.github.io/epic-fields/,,,,,3d-fields; spatial-reasoning,
 EgoVLP,model,2022-06,NeurIPS 2022,open,,2022,https://github.com/showlab/EgoVLP,https://arxiv.org/abs/2206.01670,,,,video-language-pretraining; retrieval; ego4d-transfer,
 EgoVLPv2,model,2023-07,ICCV 2023,open,,2023,https://shramanpramanick.github.io/EgoVLPv2/,https://arxiv.org/abs/2307.05463,,,,video-language-pretraining; cross-modal-fusion,
 LaViLa,model,2022-12,CVPR 2023,open,,2022,https://arxiv.org/abs/2212.04501,https://arxiv.org/abs/2212.04501,,,,video-language-representation; narration-generation,
@@ -97,17 +97,17 @@ EgoNCE++,model,2024-05,ICLR 2025,open,,2024,https://github.com/xuboshen/EgoNCEpp
 EgoDTM,model,2025-03,arXiv,open,,2025,https://github.com/xuboshen/EgoDTM,https://arxiv.org/abs/2503.15470,,,,3d-aware-vlp; depth-text-pretraining,
 EgoVLM,model,2025-06,arXiv,watch,,2025,https://arxiv.org/abs/2506.03097,https://arxiv.org/abs/2506.03097,,,,egocentric-video-reasoning; policy-optimization,
 EgoGraph,model,2026-02,arXiv,watch,,2026,https://arxiv.org/abs/2602.23709,https://arxiv.org/abs/2602.23709,,,,temporal-knowledge-graph; ultra-long-video-qa,
-Ropedia Xperience-10M Task Suite,benchmark,2026,Hugging Face,open,,2026,https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite,,,,12 embodied-AI task contracts over one public Xperience-10M sample episode,task-design; sample-evaluation; multimodal-baselines; embodied-ai,video; audio; depth; camera-pose; hand-mocap; full-body-mocap; imu; language
-Ropedia Xperience-10M Task Baselines,model,2026,Hugging Face,open,,2026,https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines,,,mit,"Public task definitions, baseline artifacts, metrics, and scale-up notes for Xperience-10M",baseline-evaluation; task-suite; multimodal-representation; qwen3-omni; cosmos,video; audio; depth; camera-pose; mocap; imu; language
 GroundVQA,model,2023-12,CVPR 2024,open,,2023,https://github.com/Becomebright/GroundVQA,https://arxiv.org/abs/2312.06505,,,,long-video-qa; temporal-grounding; ego4d-nlq,
 HiERO,model,2025-05,arXiv,watch,,2025,https://arxiv.org/abs/2505.12911,https://arxiv.org/abs/2505.12911,,,,hierarchical-activity-reasoning; egomcq; egonlq; procedure-learning,
 EgoAgent,model,2025-02,arXiv,watch,,2025,https://arxiv.org/abs/2502.05857,https://arxiv.org/abs/2502.05857,,,,joint-predictive-agent; future-state-prediction; action-prediction,
 StillFast,model,2023-04,arXiv,open,,2023,https://iplab.dmi.unict.it/stillfast/,https://arxiv.org/abs/2304.03959,,,,short-term-object-interaction-anticipation; next-active-object; ego4d,
 CONE,model,2022-11,ECCV 2022 Workshop,watch,,2022,https://arxiv.org/abs/2211.08776,https://arxiv.org/abs/2211.08776,,,,natural-language-query; video-language-grounding; ego4d-nlq,
 EgoHandTrajPred / USST,model,2023-07,ICCV 2023,open,,2023,https://actionlab-cv.github.io/EgoHandTrajPred,https://arxiv.org/abs/2307.08243,,,,3d-hand-trajectory-forecasting; anticipation; h2o; egopat3d,
-Project Aria Tools,toolkit,2022,GitHub,open,,2022,https://github.com/facebookresearch/projectaria_tools,,,,,vrs-loading; calibration; mps; gaze; trajectory,
 EPIC-KITCHENS action models,toolkit,2019,GitHub,open,,2019,https://github.com/epic-kitchens/action-models,,,,,action-recognition-baselines,
-HOMIE-toolkit,toolkit,2026,GitHub,open,,2026,https://github.com/Ropedia/HOMIE-toolkit,,,MIT,,xperience-10m-loading; hdf5-annotations; visualization; rerun; calibration; depth; mocap; imu,
 ActiveMimic,model,2026-06,arXiv,watch,,2026,https://arxiv.org/abs/2606.06194,https://arxiv.org/abs/2606.06194,,,Egocentric human-video pretraining framework that recovers synchronized hand-object and viewpoint dynamics from active perception,robot-learning; vla; egocentric-pretraining; active-perception; manipulation,egocentric-video; hand-object-motion; camera-motion
 EgoPriMo,model,2026-06,arXiv,watch,,2026,https://arxiv.org/abs/2606.08495,https://arxiv.org/abs/2606.08495,,,Egocentric motion prior for interactive humanoid control learned from egocentric human demonstrations,humanoid-control; robot-learning; vla; motion-generation; imitation-learning,egocentric-video; human-demonstrations; humanoid-motion
 FEEL,dataset,2026-03,arXiv,watch,,2026,https://arxiv.org/abs/2603.15847,https://arxiv.org/abs/2603.15847,,,"About 3M force-synchronized egocentric frames from kitchen manipulation, using custom piezoresistive gloves",physical-action-understanding; contact-understanding; hand-object; robot-learning,egocentric-video; force; glove-sensors; hand-object-contact
@@ -152,10 +152,10 @@ MARS CASTLE,model,2026-05,CVPR 2026 EgoVis,watch,,2026,https://arxiv.org/abs/260
 EgoCross Domain-Wise Inference,model,2026-05,CVPR 2026 EgoVis,watch,,2026,https://arxiv.org/abs/2606.00829,https://arxiv.org/abs/2606.00829,,,Nearly training-free domain-wise inference strategy for EgoCross source-limited egocentric video QA,egocentric-video-qa; domain-shift; challenge-solution; inference-strategy,egocentric-video; qa; support-set
 HD-EPIC Semantic-Visual Evidence,model,2026-05,CVPR 2026 EgoVis,watch,,2026,https://arxiv.org/abs/2605.29402,https://arxiv.org/abs/2605.29402,,,HD-EPIC VQA Challenge solution that separates semantic and visual evidence for efficient long-video reasoning,hd-epic-vqa; long-video-reasoning; challenge-solution,egocentric-video; video-qa; semantic-evidence; visual-evidence
 UniversalVTG,model,2026-04,arXiv,watch,,2026,https://arxiv.org/abs/2604.08522,https://arxiv.org/abs/2604.08522,,,"Lightweight cross-dataset foundation model for video temporal grounding, surfaced in the Ego4D/egocentric grounding scan",video-temporal-grounding; long-video-understanding; video-language; transfer-learning,video; text; temporal-grounding
-EgoCom / Ego audio-visual correspondence,dataset,2020,project page,open,,2020,http://vision.cs.utexas.edu/projects/ego_av_corr/,,,not specified,Egocentric video with spatial audio for conversation and audio-visual correspondence tasks,foundation-video; video-language,
 EasyCom,dataset,2021-07,arXiv,open,,2021,https://arxiv.org/abs/2107.04174,,,not specified,AR glasses egocentric multi-channel audio and wide-FOV RGB for noisy conversations,foundation-video; video-language,
 Look and Tell,dataset,2025-10,NeurIPS 2025 Workshop,watch,,2025,https://arxiv.org/abs/2510.22672,,,,"25 participants, Project Aria plus stationary cameras, gaze/speech/video, 3D reconstructions",foundation-video; video-language,
-EgoVLA,model,2025,project page,watch,,,https://rchalyang.github.io/EgoVLA/,,,,VLA training from egocentric human videos plus robot fine-tuning,robot-learning; manipulation; vla,
 EgoEngine,model,2026-06,arXiv,watch,,2026,https://egoengine.github.io/,https://arxiv.org/abs/2606.12604,,,Framework converting egocentric human manipulation videos into high-fidelity robot observation videos and executable robot action trajectories,robot-learning; manipulation; vla,
 EgoAERO,model,2026-06,arXiv,watch,,2026,https://arxiv.org/abs/2606.08057,,,,Asset-free conversion from a single egocentric RGB-D demonstration; introduces EgoDex-R in the paper,robot-learning; manipulation; vla,
 HRDexDB,dataset,2026-04,arXiv,watch,,2026,https://arxiv.org/abs/2604.14944,,,,"1.4K human/robot grasping trials, tactile, multiview video, egocentric video streams",robot-learning; manipulation; vla,
@@ -163,40 +163,40 @@ UnrealEgo2 / UnrealEgo-RW,dataset,2024-01,arXiv,watch,,2024,https://arxiv.org/ab
 TouchMoment,dataset,2026-04,CVPR 2026 Findings,watch,,2026,https://arxiv.org/abs/2604.12343,,,,"4,021 egocentric videos, 8,456 annotated hand-object contact moments",hand-object; 3d; pose,
 EgoFun3D,dataset,2026-04,arXiv,watch,,2026,https://arxiv.org/abs/2604.11038,,,,"271 egocentric videos with 3D geometry, part segmentation, articulation and function-template annotations",hand-object; 3d; pose,
 EgoEMG,dataset,2026-05,arXiv,watch,,2026,https://arxiv.org/abs/2605.05712,,,,"41 participants, bilateral EMG, IMU, RGB, external RGB-D, mocap hand labels",hand-object; 3d; pose,
-EgoEVHands,dataset,2024,GitHub,watch,,,https://github.com/ZJUWang01/EgoEV-HandPose,,,,"Stereo event-camera egocentric hand dataset with 5,419 sequences and 3D/2D keypoints",hand-object; 3d; pose,
 EventEgoHands,benchmark,2025-05,ICIP 2025,watch,,2025,https://arxiv.org/abs/2505.19169,,,,Event-based egocentric 3D hand mesh reconstruction benchmark over N-HOT3D,hand-object; 3d; pose,
 A multimodal RGB/events FPV hand dataset,dataset,2026-06,arXiv,watch,,2026,https://arxiv.org/abs/2606.10790,,,,Synthetic event-based first-person hand detection from EgoHands plus v2e,hand-object; 3d; pose,
 EgoExoMem,dataset,2026-05,arXiv,watch,,2026,https://arxiv.org/abs/2605.18734,,,,2.6K MCQs across synchronized ego-exo videos,memory; qa; assistant,
-EgoSelf,model,2025,project page,watch,,,https://abie-e.github.io/egoself_project/,,,,Personalized egocentric assistant framework with graph memory,memory; qa; assistant,
-EgoCross,dataset,2025,GitHub,watch,,,https://github.com/MyUniverse0726/EgoCross,,,,"About 1,000 QA pairs over surgery, industry, extreme sports, and animal-perspective clips",memory; qa; assistant,
-ADL Dataset,dataset,2012,project page,partial,,2012,https://www.csc.kth.se/cvap/actions/,,,,Unscripted daily activity recordings with activity/object/hand annotations in classic literature,action-recognition; procedure,
 Wrist-mounted ADL,dataset,2015-11,CVPR 2016,open,,2015,https://arxiv.org/abs/1511.06783,,,not specified,Synchronized head and wrist wearable-camera daily activities,action-recognition; procedure,
-Visual Experience Dataset / VEDB,dataset,2023,project page,partial,,2023,http://tamaraberg.com/visualexperience/,,,,240+ hours egocentric video with gaze/head tracking in classic literature,action-recognition; procedure,
-UT Ego,dataset,2012,project page,partial,,2012,http://vision.cs.utexas.edu/projects/egocentric/,,,,Long daily egocentric videos in classic summarization work,action-recognition; procedure,
-HUJI EgoSeg,dataset,2014,project page,partial,,2014,https://www.vision.huji.ac.il/egoseg/,,,,Long egocentric videos for temporal segmentation,action-recognition; procedure,
 JPL First-Person Interaction,dataset,2013,IEEE,partial,,2013,https://ieeexplore.ieee.org/document/6909626,,,,First-person videos of people interacting with a humanoid observer,action-recognition; procedure,
-FT-HID,dataset,2022,GitHub,open,,2022,https://github.com/ENDLICHERE/FT-HID,,,not specified,90K+ RGB-D first- and third-person human interaction samples from 109 subjects,action-recognition; procedure,
 LSC-ADL,dataset,2025-04,arXiv,open,,2025,https://arxiv.org/abs/2504.02060,,,not specified,ADL annotations over lifelogging data generated with clustering plus human review,action-recognition; procedure,
 SEED4D,dataset,2024-12,WACV 2025,open,adjacent,2024,https://seed4d.github.io/,https://arxiv.org/abs/2412.00730,,,"Synthetic ego-exo dynamic 4D generator and autonomous-driving dataset (16.8M images, vehicle inward/outward cameras, LiDAR; WACV 2025)",driving; 4d-reconstruction; multi-view,
-Ego4D Benchmarks,benchmark,2021,project page,benchmark,,2021,https://ego4d-data.org/,,,,"Derived benchmark suite built on Ego4D; tasks: Natural Language Query, Moment Query, episodic memory, state change, long-term anticipation, social/audio, hand-object",benchmark; evaluation,
-Ego-Exo4D Benchmarks,benchmark,2023,project page,benchmark,,2023,https://ego-exo4d-data.org/,,,,"Derived benchmark suite built on Ego-Exo4D; tasks: Fine-grained activity, proficiency, cross-view translation, 3D pose, object correspondence",benchmark; evaluation,
-EPIC-KITCHENS Challenges,benchmark,2018,project page,benchmark,,2018,https://epic-kitchens.github.io/,,,,"Derived benchmark suite built on EPIC-KITCHENS / EPIC-KITCHENS-100; tasks: Recognition, detection, anticipation, retrieval, domain adaptation",benchmark; evaluation,
 HD-EPIC VQA Challenge,benchmark,2025-02,CVPR 2025,benchmark,,2025,https://arxiv.org/abs/2502.04144,,,,"Derived benchmark suite built on HD-EPIC; tasks: Recipe, ingredient, nutrition, fine-grained action, 3D perception, object motion, gaze",benchmark; evaluation,
 EgoEnv,model,2022,project page,open,,2022,https://vision.cs.utexas.edu/projects/ego-env/,,,,Environment-aware representation learning from egocentric video,video-language; representation-learning,
-EgoMAS,model,2026,project page,open,,,https://ma-egoqa.github.io/,,,,Shared-memory baseline for multi-agent egocentric video QA,video-language; representation-learning,
 Ego-Exo representation transfer,model,2021-04,CVPR 2021,open,,2021,https://arxiv.org/abs/2104.07905,,,,Distillation from third-person video using ego-specific latent signals,action; tracking; pose; hoi,
-EgoPoseFormer,model,2024,GitHub,open,,,https://github.com/ChenhongyiYang/egoposeformer,,,,Transformer baseline for stereo egocentric 3D human pose estimation,action; tracking; pose; hoi,
 EgoSTARK,model,2023-01,arXiv,open,,2023,https://arxiv.org/abs/2301.03213,,,,Adapted long-term tracker baseline for EgoTracks,action; tracking; pose; hoi,
-EgoHOS model,model,2022,GitHub,open,,2022,https://github.com/owenzlz/EgoHOS,,,,Context-aware hand-object segmentation and augmentation pipeline,action; tracking; pose; hoi,
-AV-CONV,model,2023,project page,open,,2023,https://vjwq.github.io/AV-CONV/,,,,Audio-visual conversational graph prediction from ego/exo conversation,action; tracking; pose; hoi,
 EgoAction,model,2026-05,CVPR 2026,watch,,2026,https://arxiv.org/abs/2605.24496,,,,CVPR 2026 EPIC-KITCHENS action detection challenge pipeline,action; tracking; pose; hoi,
 EgoAdapt,model,2026-05,CVPR 2026,watch,,2026,https://arxiv.org/abs/2605.24500,,,,CVPR 2026 HD-EPIC VQA challenge inference-time adaptation pipeline,action; tracking; pose; hoi,
-Ego4D CLI and docs,toolkit,2021,project page,open,,2021,https://ego4d-data.org/,,,,Downloading and working with Ego4D data after license approval.,tooling,
-Ego-Exo4D CLI and docs,toolkit,2023,project page,open,,2023,https://ego-exo4d-data.org/,,,,Downloading synchronized ego-exo data and annotations.,tooling,
-VISOR API,toolkit,2022,GitHub,open,,2022,https://github.com/epic-kitchens/VISOR,,,,Loading dense EPIC-KITCHENS hand/object masks and relations.,tooling,
-EgoObjects API,toolkit,2023,GitHub,open,,2023,https://github.com/facebookresearch/EgoObjects,,,,Working with category and instance-level egocentric object labels.,tooling,
-HOT3D tooling,toolkit,2024,project page,open,,,https://facebookresearch.github.io/hot3d/,,,,Loading HOT3D hand/object/camera pose annotations and models.,tooling,
-HOI4D tooling,toolkit,2022,project page,open,,2022,https://hoi4d.github.io/,,,,"Loading RGB-D frames, point clouds, object meshes, and pose/segmentation annotations.",tooling,
 EgoVid-5M,dataset,2024-11,NeurIPS 2025,open,,2024,https://egovid.github.io/,https://arxiv.org/abs/2411.08380,,not specified,5M curated egocentric clips at 1080p with fine-grained kinematic and high-level textual action annotations (NeurIPS 2025),video-generation; world-modeling; action-conditioned-generation,
 AoE,dataset,2026-02,arXiv,watch,,2026,https://arxiv.org/abs/2602.23893,,,,Always-on egocentric human video collection pipeline and corpus for embodied AI data scaling,robot-learning; data-collection; embodied-ai,
 Seeing Across Views (MV-RoboBench),benchmark,2025-10,ICLR 2026,open,adjacent,2025,https://github.com/microsoft/MV-RoboBench,https://arxiv.org/abs/2510.19400,,,MV-RoboBench: 1.7K curated QA items over eight subtasks testing multi-view spatial reasoning of VLMs in robotic manipulation scenes (ICLR 2026),multi-view; robot-manipulation; vlm-evaluation,
@@ -204,10 +204,10 @@ EgoGesture,dataset,2017,IEEE TMM 2018,open,,2017,https://ieeexplore.ieee.org/doc
 EgoBrain,dataset,2025-06,arXiv,watch,,2025,https://arxiv.org/abs/2506.01353,,,,Synchronized EEG and egocentric video for human action understanding from minds and eyes,eeg; action-understanding; multimodal,
 MM-Ego,model,2024-10,ICLR 2025,open,,2024,https://arxiv.org/abs/2410.07177,,,,"Egocentric multimodal LLM with Memory Pointer Prompting; 7M-sample QA data engine and the EgoMemoria benchmark (629 videos, 7,026 questions)",video-language; memory; qa,
 EgoStream,benchmark,2026-05,arXiv,watch,,2026,https://arxiv.org/abs/2605.31557,,,,"2,250 questions across seven memory dimensions with Answer Validity Windows, expanded to 8,528 recall-conditioned evaluations over streams up to 45.3 hours",streaming-memory; episodic-memory; qa,
-EgoMemory,benchmark,2026,OpenReview,watch,,2026,https://openreview.net/forum?id=T0em4hJCQb,,,,"165,795 user-specific object annotations over 245 videos from 45 participants for memory-augmented personalized retrieval",personalized-retrieval; episodic-memory; long-context,
-EgoTextVQA,benchmark,2025,CVPR 2025,open,,2025,https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_EgoTextVQA_Towards_Egocentric_Scene-Text_Aware_Video_Question_Answering_CVPR_2025_paper.pdf,,,,Egocentric scene-text-aware video QA across indoor housekeeping and outdoor driving scenes (CVPR 2025),scene-text; qa; reading,
 Gesture-Based Egocentric Video QA,benchmark,2026-03,CVPR 2026,watch,,2026,https://arxiv.org/abs/2603.12533,,,,Egocentric video QA grounded in the camera wearer's pointing and deictic gestures,gesture-grounding; qa; referential,
-EgoVQA,benchmark,2019,ICCV 2019,open,,2019,https://openaccess.thecvf.com/content_ICCVW_2019/html/EPIC/Fan_EgoVQA_-_An_Egocentric_Video_Question_Answering_Benchmark_Dataset_ICCVW_2019_paper.html,,,,600+ QA pairs over egocentric videos; an early first-person video question answering benchmark,qa; action; classic,
 ExAct,benchmark,2025-06,arXiv,open,,2025,https://arxiv.org/abs/2506.06277,,,,Video-language benchmark for expert action analysis and feedback over skilled egocentric/exocentric activity,expert-feedback; skill-assessment; video-language,
 Home Action Genome / HOMAGE,dataset,2021-05,CVPR 2021,open,,2021,https://homeactiongenome.org/,https://arxiv.org/abs/2105.05226,,not specified,"27 participants, multi-modal synchronized ego and third-person views with 12 sensor types and hierarchical activity/action labels in home settings",action-recognition; multi-view; compositional,
 EgoExo-Fitness,dataset,2024-06,ECCV 2024,open,,2024,https://github.com/iSEE-Laboratory/EgoExo-Fitness,https://arxiv.org/abs/2406.08877,,Apache-2.0,"Synchronized ego and exo fitness videos with keypoint verification, execution comments, and action quality scores (ECCV 2024)",ego-exo; action-quality; skill-assessment,
@@ -243,7 +243,7 @@ Temporal-Aware Ego VLM,model,2026-03,arXiv,watch,,2026,https://arxiv.org/abs/260
 EgoMotion,model,2026-04,arXiv,watch,,2026,https://arxiv.org/abs/2604.19105,https://arxiv.org/abs/2604.19105,,,Hierarchical reasoning plus diffusion framework for egocentric vision-language motion generation from first-person context,motion; motion-estimation; video-language,egocentric-video; motion; text
 Gaze-SoM HOI Anticipation,model,2026-04,ICPR 2026,watch,,2026,https://arxiv.org/abs/2604.03667,https://arxiv.org/abs/2604.03667,,,Leverages gaze and set-of-mark prompting in vision-language LLMs for hand-object-interaction anticipation from egocentric video,anticipation; hand-object; gaze-reasoning,egocentric-video; gaze; text
 Sanpo-D,benchmark,2026-01,arXiv,watch,,2026,https://arxiv.org/abs/2601.18100,https://arxiv.org/abs/2601.18100,,,"Fine-grained re-annotation of the Sanpo egocentric navigation data with spatial signals, benchmarking spatial-conditioned reasoning of VLMs over long first-person videos",spatial-reasoning; navigation; video-qa,egocentric-video; qa
-EgoSurgery,dataset,2024,MICCAI 2024,open,,2024,https://github.com/Fujiry0/EgoSurgery,https://arxiv.org/abs/2503.18755,https://github.com/Fujiry0/EgoSurgery,custom,Egocentric open-surgery video dataset family: EgoSurgery-Phase for surgical phase recognition and EgoSurgery-HTS for pixel-wise hand-tool segmentation of 14 surgical tools (MICCAI 2024),action-recognition; hand-object-segmentation; procedural-task-assistance,egocentric-video; segmentation; object-boxes
 EgoEMS,dataset,2025-11,AAAI 2026,watch,,2025,https://arxiv.org/abs/2511.09894,https://arxiv.org/abs/2511.09894,,,"High-fidelity multimodal egocentric dataset for cognitive assistance in emergency medical services, capturing time-critical team actions in high-stakes scenarios",procedural-task-assistance; action-recognition; real-time-understanding,egocentric-video; audio; multimodal-annotations
 LEMMA,dataset,2020-07,ECCV 2020,open,,2020,https://arxiv.org/abs/2007.15781,https://arxiv.org/abs/2007.15781,,not specified,"Multi-view multi-agent multi-task daily-activity dataset across 14 kitchens and living rooms, densely annotated with atomic actions and human-object interactions (ECCV 2020)",compositional; action-recognition; anticipation,egocentric-video; third-person-video; multimodal-annotations
 EgoMe,dataset,2025-01,arXiv,watch,,2025,https://arxiv.org/abs/2501.19061,https://arxiv.org/abs/2501.19061,,,"Real-world dataset and challenge for following a demonstrator via egocentric view, pairing exocentric demonstrations with egocentric imitation across everyday tasks",imitation-learning; cross-view; action-understanding,egocentric-video; exocentric-video
@@ -313,9 +313,9 @@ EgoSplat,model,2025-03,arXiv,watch,,2025,https://arxiv.org/abs/2503.11345,https:
 3D-Aware Ego Instance Tracking,model,2024-08,arXiv,watch,,2024,https://arxiv.org/abs/2408.09860,https://arxiv.org/abs/2408.09860,,,3D-aware instance segmentation and multi-object tracking in egocentric video that lifts 2D masks with scene geometry to survive rapid motion and occlusion (large ID-switch reductions on EPIC Fields),detection-segmentation; tracking; three-d-and-scene,egocentric-video; camera-pose
 EgoCogNav,model,2025-11,arXiv,watch,,2025,https://arxiv.org/abs/2511.17581,https://arxiv.org/abs/2511.17581,,,"Cognition-aware egocentric navigation framework that forecasts human trajectory and head motion from perceived path uncertainty, modeling scanning, hesitation, and backtracking, with the 6-hour CEN dataset",ar-sensing-navigation; reasoning-intent-planning; pose-and-body,egocentric-video; motion
 Neck-Mounted Gaze (GLC),model,2026-02,arXiv,watch,,2026,https://arxiv.org/abs/2602.11669,https://arxiv.org/abs/2602.11669,,,"Transformer gaze estimator (GLC) for a shoulder-level neck-mounted camera with an out-of-bound gaze classification task and head/neck multi-view co-learning, plus a ~4-hour 8-subject dataset",ar-sensing-navigation; pose-and-body,egocentric-video; gaze
-CMU-MMAC,dataset,2009,CMU tech report 2009,open,,2009,http://kitchen.cs.cmu.edu/,http://kitchen.cs.cmu.edu/,,not specified,"Among the earliest egocentric datasets: a multimodal kitchen-activity database with head-mounted egocentric video plus body IMUs, motion capture, and audio for 43 subjects cooking 5 recipes",action-and-procedure; detection-segmentation,egocentric-video; imu; motion; audio
-First-Person Social Interactions,dataset,2012,CVPR 2012,open,,2012,http://ai.stanford.edu/~alireza/publication/CVPR12.pdf,http://ai.stanford.edu/~alireza/publication/CVPR12.pdf,,not specified,"The first egocentric social-interaction dataset: day-long head-mounted video of 8 subjects at a theme park, annotated for social interactions, roles, attention, and turn-taking",audio-and-social; reasoning-intent-planning; detection-segmentation,egocentric-video
-BEOID,dataset,2014,BMVC 2014,open,,2014,https://dimadamen.github.io/BEOID/,https://dimadamen.github.io/BEOID/,,not specified,"Gaze-tracked egocentric video of 8 users interacting with objects across 6 everyday locations (kitchen, workspace, printer, corridor, gym) for discovering task-relevant objects and interaction modes",hand-object-interaction; action-and-procedure; detection-segmentation,egocentric-video; gaze
 DreamDojo,model,2026-02,ICML 2026,open,,2026,https://github.com/NVIDIA/DreamDojo,https://arxiv.org/abs/2602.06949,https://github.com/NVIDIA/DreamDojo,,"Generalist robot world model pretrained on 44K hours of egocentric human video using continuous latent actions; distilled to real-time (10.8 FPS) for teleoperation, policy evaluation, and model-based planning",world-modeling; robot-learning; video-generation,egocentric-video; latent-actions
 UniDex,model,2026-03,CVPR 2026,watch,,2026,https://arxiv.org/abs/2603.22264,https://arxiv.org/abs/2603.22264,,,"Robot foundation suite for universal dexterous hand control learned from egocentric human videos, transferring human manipulation priors to multi-fingered robot hands",robot-learning; dexterous-manipulation; vla,egocentric-video; hand-pose
 DexGloveHOI,dataset,2026-05,arXiv,watch,,2026,https://arxiv.org/abs/2605.21714,https://arxiv.org/abs/2605.21714,,,100K+ synchronized egocentric vision and on-glove IMU samples with marker-based mocap ground-truth 3D hand pose across dexterous daily object manipulation (introduced with the AVI-HT vision-IMU fusion method),pose-and-body; hand-object-interaction; 3d-4d-understanding,egocentric-video; imu; hand-pose
@@ -458,4 +458,4 @@ Ego-Engagement,dataset,2016-04,arXiv,watch,,2016,https://arxiv.org/abs/1604.0090
 First-Person Activity Forecasting,model,2016-12,ICCV 2017 Oral,open,,2016,https://arxiv.org/abs/1612.07796,https://arxiv.org/abs/1605.09070,https://darko.cs.utexas.edu/,,DARKO online inverse-reinforcement-learning system for forecasting actions and active objects in first-person video,anticipation; action-recognition; active-object-detection,egocentric-video; object-boxes
 Ego-Object Discovery / EDUB,dataset,2015-04,arXiv,open,,2015,https://arxiv.org/abs/1504.01639,https://arxiv.org/abs/1504.01639,,unknown-public,"EDUB egocentric photo-stream dataset for unsupervised object discovery, with 4,912 daily-life images from four users and public code/data reported by the paper",object-discovery; detection-segmentation; lifelogging,egocentric-images; object-boxes
 Egocentric FOV Localization,model,2015-01,WACV 2015,watch,,2015,https://arxiv.org/abs/1501.01643,https://arxiv.org/abs/1501.01643,,,Localizes the first-person camera wearer's field of view in surveillance/top-view video using wearable point-of-view devices,fov-localization; cross-view; person-localization,egocentric-video; overhead-video
-First-Person Pose Recognition,model,2015,CVPR 2015,watch,,2015,https://openaccess.thecvf.com/content_cvpr_2015/html/Rogez_First-Person_Pose_Recognition_2015_CVPR_paper.html,https://openaccess.thecvf.com/content_cvpr_2015/html/Rogez_First-Person_Pose_Recognition_2015_CVPR_paper.html,,,Egocentric workspace method for recognizing human pose from first-person camera geometry and likely body configurations,pose-and-body; egocentric-3d-pose; wearable-sensing,egocentric-video; body-pose

 name,kind,released,venue,status,scope,year,url,paper,code,license,scale,tasks,modalities
 Xperience-10M,dataset,2026-03,Hugging Face,request,,2026,https://huggingface.co/datasets/ropedia-ai/xperience-10m,,,other,"10M experiences, 10K hours, six video streams, audio, stereo depth, camera pose, hand/body mocap, IMU, hierarchical language, about 1 PB total",embodied-ai; world-modeling; robot-learning; sensor-fusion; 3d-4d-understanding; imitation-learning,fisheye-video; stereo-video; audio; stereo-depth; camera-pose; hand-mocap; full-body-mocap; imu; hierarchical-language
+Xperience-10M Sample,dataset,2026-03,Hugging Face,open,,2026,https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample,,,cc-by-nc-4.0,Public sample episode; Hugging Face viewer reports 6 rows,sample-data; loader-testing; visualization; task-suite-prototyping,video; hdf5-annotations
 Ego4D,dataset,2021-10,CVPR 2022,request,,2021,https://ego4d-data.org/,https://arxiv.org/abs/2110.07058,https://github.com/facebookresearch/Ego4d,Ego4D License Agreement,"3,670+ hours, 900+ camera wearers, 74 locations, 9 countries",episodic-memory; nlq; moment-query; hand-object; forecasting; social; audio-visual,video; audio; gaze; stereo; 3d; narrations
 Ego-Exo4D,dataset,2023-11,CVPR 2024,request,,2023,https://ego-exo4d-data.org/,https://arxiv.org/abs/2311.18259,https://github.com/facebookresearch/projectaria_tools,Ego-Exo4D License Agreement,"1,286 hours, 740 participants, synchronized ego/exo views",ego-exo; skilled-activity; cross-view; proficiency; pose,video; audio; gaze; imu; 3d-point-clouds; camera-poses; language
 EPIC-KITCHENS-100,dataset,2020-06,IJCV 2022,open,,2020,https://epic-kitchens.github.io/,https://arxiv.org/abs/2006.13256,https://github.com/epic-kitchens/epic-kitchens-100-annotations,CC BY-NC 4.0,"100 hours, 20M frames, 90K action segments, 45 kitchens",action-recognition; action-detection; anticipation; retrieval; domain-adaptation,video; audio; narrations; object-boxes
 HOI4D,dataset,2022-03,CVPR 2022,open,,2022,https://hoi4d.github.io/,https://arxiv.org/abs/2203.01577,,CC BY-NC 4.0,"2.4M RGB-D egocentric frames, 4,000+ sequences, 800 objects",4d-hoi; pose-tracking; action-segmentation,rgbd; point-clouds; 3d-hand-pose; object-pose; segmentation
 H2O,dataset,2021-04,ICCV 2021,open,,2021,https://arxiv.org/abs/2104.11181,https://arxiv.org/abs/2104.11181,,not specified,,first-person-interaction-recognition; two-hand-pose; hand-object-pose,multiview-rgbd; 3d-hand-pose; object-pose; camera-pose; object-meshes; scene-point-clouds
 ARCTIC,dataset,2022-04,CVPR 2023,request,,2022,https://arctic.is.tue.mpg.de/,https://arxiv.org/abs/2204.13662,,,2.1M frames,bimanual-manipulation; reconstruction; interaction-field-estimation,video; hand-meshes; object-meshes; contact; articulated-objects
+EgoHands,dataset,2015-12,ICCV 2015,open,,2015,http://vision.soic.indiana.edu/projects/egohands/,,,not specified,"48 Google Glass videos, 4,800 annotated images",hand-detection; hand-segmentation,rgb; hand-masks
+FPHA,dataset,2017-04,CVPR 2018,open,,2017,https://guiggh.github.io/publications/first-person-hands/,https://arxiv.org/abs/1704.02463,,not specified,"100K+ RGB-D frames, 45 action classes, 26 objects",hand-action-recognition; 3d-hand-pose,rgbd; 3d-hand-pose; object-pose
 EgoHOS,dataset,2022-08,ECCV 2022,open,,2022,https://github.com/owenzlz/EgoHOS,https://arxiv.org/abs/2208.03826,,MIT,"11,243 egocentric images",hand-object-segmentation; contact-understanding,rgb; hand-object-contact-masks
 Ego2Hands,dataset,2020-11,arXiv,open,,2020,https://arxiv.org/abs/2011.07252,https://arxiv.org/abs/2011.07252,,not specified,,two-hand-segmentation; hand-detection,rgb; synthetic-composited-masks
 Ego2HandsPose,dataset,2022-06,arXiv,open,,2022,https://arxiv.org/abs/2206.04927,https://arxiv.org/abs/2206.04927,,not specified,,two-hand-3d-pose,rgb; 3d-hand-pose
+EgoBody,dataset,2021-12,ECCV 2022,open,,2021,https://sanweiliti.github.io/egobody/egobody.html,https://arxiv.org/abs/2112.07642,,not specified,,3d-human-pose; body-shape; motion,hololens2; rgb; depth; gaze; head-pose; hand-pose; body-pose
 GIMO,dataset,2022-04,ECCV 2022,open,,2022,https://github.com/y-zheng18/GIMO,https://arxiv.org/abs/2204.09443,,not specified,,gaze-informed-motion-prediction; human-motion; scene-context,egocentric-video; gaze; body-pose; scene-scans
 EgoHumans,benchmark,2023-05,ICCV 2023,partial,,2023,https://arxiv.org/abs/2305.16487,https://arxiv.org/abs/2305.16487,,,125K+ egocentric images,multi-human-tracking; 3d-pose; mesh-recovery,egocentric-video; multiview-video; 2d-pose; 3d-pose; mesh
 UnrealEgo,dataset,2022-08,ECCV 2022,open,,2022,https://4dqv.mpi-inf.mpg.de/UnrealEgo/,https://arxiv.org/abs/2208.01633,,not specified,,egocentric-3d-pose,synthetic-stereo; 3d-human-pose
+xR-EgoPose,dataset,2019-07,GitHub,open,,2019,https://github.com/facebookresearch/xR-EgoPose,https://arxiv.org/abs/1907.10045,,custom,,xr-pose-estimation,synthetic-egocentric; 3d-human-pose
 EgoGTA / EgoPW-Scene,dataset,2022-12,arXiv,partial,,2022,https://arxiv.org/abs/2212.11684,https://arxiv.org/abs/2212.11684,,,,scene-aware-3d-human-pose; egocentric-depth; human-scene-interaction,synthetic-egocentric; scene-depth; human-pose
 EgoTracks,benchmark,2023-01,NeurIPS 2023,open,,2023,https://arxiv.org/abs/2301.03213,https://arxiv.org/abs/2301.03213,,,,long-term-object-tracking; redetection,
+TREK-150,benchmark,2021-08,project page,open,,2021,https://machinelearning.uniud.it/datasets/trek150/,https://arxiv.org/abs/2108.13665,,,,single-object-tracking,
+Project Aria Datasets,collection,2023-08,arXiv,open,,2023,https://www.projectaria.com/datasets/,https://arxiv.org/abs/2308.13561,,,,ar-perception; scene-understanding; wearable-sensing,aria; vrs; calibration; gaze; imu; mps
 Aria Digital Twin,dataset,2023-06,arXiv,open,,2023,https://www.projectaria.com/datasets/adt/,https://arxiv.org/abs/2306.06362,,CC BY-NC-SA 4.0,"200 sequences, 398 object instances",3d-machine-perception; object-tracking; scene-reconstruction,aria; rgb; monochrome; imu; 6dof; depth; segmentation; synthetic-rendering
 Aria Everyday Activities,dataset,2024-02,arXiv,open,,2024,https://www.projectaria.com/datasets/aea/,https://arxiv.org/abs/2402.13349,,CC BY-NC-SA 4.0,143 daily activity sequences across five indoor locations,everyday-activity; neural-scene-reconstruction; prompted-segmentation,aria; trajectory; point-cloud; gaze; speech
 Nymeria,dataset,2024-06,ECCV 2024,open,,2024,https://www.projectaria.com/datasets/nymeria/,https://arxiv.org/abs/2406.09905,,CC BY-NC-SA 4.0,"300 hours, 264 participants, 50 locations",motion-language; body-tracking; action-recognition,aria; eye-tracking; imu; body-motion; language; observer-view
 KrishnaCam / OAK,dataset,2021-08,ICCV 2021,partial,,2021,https://oakdata.github.io/,https://arxiv.org/abs/2108.11005,,,"OAK: 80 snippets, about 17.5 hours, 105 object categories from KrishnaCam",continual-learning; object-detection; lifelogging,egocentric-video; object-boxes
 EgoK360,dataset,2020-10,ICIP 2020,partial,,2020,https://egok360.github.io/,https://arxiv.org/abs/2010.08055,,,,360-video; activity-recognition,first-person-360-video; action-labels
 EgoTraj,dataset,2026-05,arXiv,open,,2026,https://github.com/yehiahmad/EgoTraj,https://arxiv.org/abs/2605.19004,,custom,75 Meta Quest Pro navigation sequences,trajectory-prediction; navigation; assistive-systems,rgb; head-pose; gaze; scene-labels
+VISOR,benchmark,2022-09,NeurIPS 2022,open,,2022,https://epic-kitchens.github.io/VISOR/,https://arxiv.org/abs/2209.08199,,cc-by-nc-4.0,,hand-segmentation; active-object-segmentation; relations,
+EPIC-Sounds,benchmark,2023-02,ICASSP 2023,open,,2023,https://epic-kitchens.github.io/epic-sounds/,https://arxiv.org/abs/2302.00646,,,,audio-event-recognition,
+EPIC-Fields,benchmark,2023-06,NeurIPS 2023,open,,2023,https://epic-kitchens.github.io/epic-fields/,https://arxiv.org/abs/2306.08731,,,,3d-fields; spatial-reasoning,
 EgoVLP,model,2022-06,NeurIPS 2022,open,,2022,https://github.com/showlab/EgoVLP,https://arxiv.org/abs/2206.01670,,,,video-language-pretraining; retrieval; ego4d-transfer,
 EgoVLPv2,model,2023-07,ICCV 2023,open,,2023,https://shramanpramanick.github.io/EgoVLPv2/,https://arxiv.org/abs/2307.05463,,,,video-language-pretraining; cross-modal-fusion,
 LaViLa,model,2022-12,CVPR 2023,open,,2022,https://arxiv.org/abs/2212.04501,https://arxiv.org/abs/2212.04501,,,,video-language-representation; narration-generation,
 EgoDTM,model,2025-03,arXiv,open,,2025,https://github.com/xuboshen/EgoDTM,https://arxiv.org/abs/2503.15470,,,,3d-aware-vlp; depth-text-pretraining,
 EgoVLM,model,2025-06,arXiv,watch,,2025,https://arxiv.org/abs/2506.03097,https://arxiv.org/abs/2506.03097,,,,egocentric-video-reasoning; policy-optimization,
 EgoGraph,model,2026-02,arXiv,watch,,2026,https://arxiv.org/abs/2602.23709,https://arxiv.org/abs/2602.23709,,,,temporal-knowledge-graph; ultra-long-video-qa,
+Ropedia Xperience-10M Task Suite,benchmark,2026-03,Hugging Face,open,,2026,https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite,,,,12 embodied-AI task contracts over one public Xperience-10M sample episode,task-design; sample-evaluation; multimodal-baselines; embodied-ai,video; audio; depth; camera-pose; hand-mocap; full-body-mocap; imu; language
+Ropedia Xperience-10M Task Baselines,model,2026-03,Hugging Face,open,,2026,https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines,,,mit,"Public task definitions, baseline artifacts, metrics, and scale-up notes for Xperience-10M",baseline-evaluation; task-suite; multimodal-representation; qwen3-omni; cosmos,video; audio; depth; camera-pose; mocap; imu; language
 GroundVQA,model,2023-12,CVPR 2024,open,,2023,https://github.com/Becomebright/GroundVQA,https://arxiv.org/abs/2312.06505,,,,long-video-qa; temporal-grounding; ego4d-nlq,
 HiERO,model,2025-05,arXiv,watch,,2025,https://arxiv.org/abs/2505.12911,https://arxiv.org/abs/2505.12911,,,,hierarchical-activity-reasoning; egomcq; egonlq; procedure-learning,
 EgoAgent,model,2025-02,arXiv,watch,,2025,https://arxiv.org/abs/2502.05857,https://arxiv.org/abs/2502.05857,,,,joint-predictive-agent; future-state-prediction; action-prediction,
 StillFast,model,2023-04,arXiv,open,,2023,https://iplab.dmi.unict.it/stillfast/,https://arxiv.org/abs/2304.03959,,,,short-term-object-interaction-anticipation; next-active-object; ego4d,
 CONE,model,2022-11,ECCV 2022 Workshop,watch,,2022,https://arxiv.org/abs/2211.08776,https://arxiv.org/abs/2211.08776,,,,natural-language-query; video-language-grounding; ego4d-nlq,
 EgoHandTrajPred / USST,model,2023-07,ICCV 2023,open,,2023,https://actionlab-cv.github.io/EgoHandTrajPred,https://arxiv.org/abs/2307.08243,,,,3d-hand-trajectory-forecasting; anticipation; h2o; egopat3d,
+Project Aria Tools,toolkit,2023-08,arXiv,open,,2023,https://github.com/facebookresearch/projectaria_tools,https://arxiv.org/abs/2308.13561,,,,vrs-loading; calibration; mps; gaze; trajectory,
 EPIC-KITCHENS action models,toolkit,2019,GitHub,open,,2019,https://github.com/epic-kitchens/action-models,,,,,action-recognition-baselines,
+HOMIE-toolkit,toolkit,2026-03,GitHub,open,,2026,https://github.com/Ropedia/HOMIE-toolkit,,,MIT,,xperience-10m-loading; hdf5-annotations; visualization; rerun; calibration; depth; mocap; imu,
 ActiveMimic,model,2026-06,arXiv,watch,,2026,https://arxiv.org/abs/2606.06194,https://arxiv.org/abs/2606.06194,,,Egocentric human-video pretraining framework that recovers synchronized hand-object and viewpoint dynamics from active perception,robot-learning; vla; egocentric-pretraining; active-perception; manipulation,egocentric-video; hand-object-motion; camera-motion
 EgoPriMo,model,2026-06,arXiv,watch,,2026,https://arxiv.org/abs/2606.08495,https://arxiv.org/abs/2606.08495,,,Egocentric motion prior for interactive humanoid control learned from egocentric human demonstrations,humanoid-control; robot-learning; vla; motion-generation; imitation-learning,egocentric-video; human-demonstrations; humanoid-motion
 FEEL,dataset,2026-03,arXiv,watch,,2026,https://arxiv.org/abs/2603.15847,https://arxiv.org/abs/2603.15847,,,"About 3M force-synchronized egocentric frames from kitchen manipulation, using custom piezoresistive gloves",physical-action-understanding; contact-understanding; hand-object; robot-learning,egocentric-video; force; glove-sensors; hand-object-contact
 EgoCross Domain-Wise Inference,model,2026-05,CVPR 2026 EgoVis,watch,,2026,https://arxiv.org/abs/2606.00829,https://arxiv.org/abs/2606.00829,,,Nearly training-free domain-wise inference strategy for EgoCross source-limited egocentric video QA,egocentric-video-qa; domain-shift; challenge-solution; inference-strategy,egocentric-video; qa; support-set
 HD-EPIC Semantic-Visual Evidence,model,2026-05,CVPR 2026 EgoVis,watch,,2026,https://arxiv.org/abs/2605.29402,https://arxiv.org/abs/2605.29402,,,HD-EPIC VQA Challenge solution that separates semantic and visual evidence for efficient long-video reasoning,hd-epic-vqa; long-video-reasoning; challenge-solution,egocentric-video; video-qa; semantic-evidence; visual-evidence
 UniversalVTG,model,2026-04,arXiv,watch,,2026,https://arxiv.org/abs/2604.08522,https://arxiv.org/abs/2604.08522,,,"Lightweight cross-dataset foundation model for video temporal grounding, surfaced in the Ego4D/egocentric grounding scan",video-temporal-grounding; long-video-understanding; video-language; transfer-learning,video; text; temporal-grounding
+EgoCom / Ego audio-visual correspondence,dataset,2023-07,arXiv,open,,2023,http://vision.cs.utexas.edu/projects/ego_av_corr/,https://arxiv.org/abs/2307.04760,,not specified,Egocentric video with spatial audio for conversation and audio-visual correspondence tasks,foundation-video; video-language,
 EasyCom,dataset,2021-07,arXiv,open,,2021,https://arxiv.org/abs/2107.04174,,,not specified,AR glasses egocentric multi-channel audio and wide-FOV RGB for noisy conversations,foundation-video; video-language,
 Look and Tell,dataset,2025-10,NeurIPS 2025 Workshop,watch,,2025,https://arxiv.org/abs/2510.22672,,,,"25 participants, Project Aria plus stationary cameras, gaze/speech/video, 3D reconstructions",foundation-video; video-language,
+EgoVLA,model,2025-07,project page,watch,,,https://rchalyang.github.io/EgoVLA/,,,,VLA training from egocentric human videos plus robot fine-tuning,robot-learning; manipulation; vla,
 EgoEngine,model,2026-06,arXiv,watch,,2026,https://egoengine.github.io/,https://arxiv.org/abs/2606.12604,,,Framework converting egocentric human manipulation videos into high-fidelity robot observation videos and executable robot action trajectories,robot-learning; manipulation; vla,
 EgoAERO,model,2026-06,arXiv,watch,,2026,https://arxiv.org/abs/2606.08057,,,,Asset-free conversion from a single egocentric RGB-D demonstration; introduces EgoDex-R in the paper,robot-learning; manipulation; vla,
 HRDexDB,dataset,2026-04,arXiv,watch,,2026,https://arxiv.org/abs/2604.14944,,,,"1.4K human/robot grasping trials, tactile, multiview video, egocentric video streams",robot-learning; manipulation; vla,
 TouchMoment,dataset,2026-04,CVPR 2026 Findings,watch,,2026,https://arxiv.org/abs/2604.12343,,,,"4,021 egocentric videos, 8,456 annotated hand-object contact moments",hand-object; 3d; pose,
 EgoFun3D,dataset,2026-04,arXiv,watch,,2026,https://arxiv.org/abs/2604.11038,,,,"271 egocentric videos with 3D geometry, part segmentation, articulation and function-template annotations",hand-object; 3d; pose,
 EgoEMG,dataset,2026-05,arXiv,watch,,2026,https://arxiv.org/abs/2605.05712,,,,"41 participants, bilateral EMG, IMU, RGB, external RGB-D, mocap hand labels",hand-object; 3d; pose,
+EgoEVHands,dataset,2026-05,arXiv,watch,,2026,https://github.com/ZJUWang01/EgoEV-HandPose,https://arxiv.org/abs/2605.12297,,,"Stereo event-camera egocentric hand dataset with 5,419 sequences and 3D/2D keypoints",hand-object; 3d; pose,
 EventEgoHands,benchmark,2025-05,ICIP 2025,watch,,2025,https://arxiv.org/abs/2505.19169,,,,Event-based egocentric 3D hand mesh reconstruction benchmark over N-HOT3D,hand-object; 3d; pose,
 A multimodal RGB/events FPV hand dataset,dataset,2026-06,arXiv,watch,,2026,https://arxiv.org/abs/2606.10790,,,,Synthetic event-based first-person hand detection from EgoHands plus v2e,hand-object; 3d; pose,
 EgoExoMem,dataset,2026-05,arXiv,watch,,2026,https://arxiv.org/abs/2605.18734,,,,2.6K MCQs across synchronized ego-exo videos,memory; qa; assistant,
+EgoSelf,model,2026-04,arXiv,watch,,2026,https://abie-e.github.io/egoself_project/,https://arxiv.org/abs/2604.19564,,,Personalized egocentric assistant framework with graph memory,memory; qa; assistant,
+EgoCross,dataset,2025-08,arXiv,watch,,2025,https://github.com/MyUniverse0726/EgoCross,https://arxiv.org/abs/2508.10729,,,"About 1,000 QA pairs over surgery, industry, extreme sports, and animal-perspective clips",memory; qa; assistant,
+ADL Dataset,dataset,2012-06,project page,partial,,2012,https://www.csc.kth.se/cvap/actions/,,,,Unscripted daily activity recordings with activity/object/hand annotations in classic literature,action-recognition; procedure,
 Wrist-mounted ADL,dataset,2015-11,CVPR 2016,open,,2015,https://arxiv.org/abs/1511.06783,,,not specified,Synchronized head and wrist wearable-camera daily activities,action-recognition; procedure,
+Visual Experience Dataset / VEDB,dataset,2024-02,arXiv,partial,,2024,http://tamaraberg.com/visualexperience/,https://arxiv.org/abs/2404.18934,,,240+ hours egocentric video with gaze/head tracking in classic literature,action-recognition; procedure,
+UT Ego,dataset,2012-06,project page,partial,,2012,http://vision.cs.utexas.edu/projects/egocentric/,,,,Long daily egocentric videos in classic summarization work,action-recognition; procedure,
+HUJI EgoSeg,dataset,2014-06,project page,partial,,2014,https://www.vision.huji.ac.il/egoseg/,,,,Long egocentric videos for temporal segmentation,action-recognition; procedure,
 JPL First-Person Interaction,dataset,2013,IEEE,partial,,2013,https://ieeexplore.ieee.org/document/6909626,,,,First-person videos of people interacting with a humanoid observer,action-recognition; procedure,
+FT-HID,dataset,2022-09,GitHub,open,,2022,https://github.com/ENDLICHERE/FT-HID,https://arxiv.org/abs/2209.10155,,not specified,90K+ RGB-D first- and third-person human interaction samples from 109 subjects,action-recognition; procedure,
 LSC-ADL,dataset,2025-04,arXiv,open,,2025,https://arxiv.org/abs/2504.02060,,,not specified,ADL annotations over lifelogging data generated with clustering plus human review,action-recognition; procedure,
 SEED4D,dataset,2024-12,WACV 2025,open,adjacent,2024,https://seed4d.github.io/,https://arxiv.org/abs/2412.00730,,,"Synthetic ego-exo dynamic 4D generator and autonomous-driving dataset (16.8M images, vehicle inward/outward cameras, LiDAR; WACV 2025)",driving; 4d-reconstruction; multi-view,
+Ego4D Benchmarks,benchmark,2021-10,project page,benchmark,,2021,https://ego4d-data.org/,,,,"Derived benchmark suite built on Ego4D; tasks: Natural Language Query, Moment Query, episodic memory, state change, long-term anticipation, social/audio, hand-object",benchmark; evaluation,
+Ego-Exo4D Benchmarks,benchmark,2023-11,project page,benchmark,,2023,https://ego-exo4d-data.org/,,,,"Derived benchmark suite built on Ego-Exo4D; tasks: Fine-grained activity, proficiency, cross-view translation, 3D pose, object correspondence",benchmark; evaluation,
+EPIC-KITCHENS Challenges,benchmark,2018-04,project page,benchmark,,2018,https://epic-kitchens.github.io/,,,,"Derived benchmark suite built on EPIC-KITCHENS / EPIC-KITCHENS-100; tasks: Recognition, detection, anticipation, retrieval, domain adaptation",benchmark; evaluation,
 HD-EPIC VQA Challenge,benchmark,2025-02,CVPR 2025,benchmark,,2025,https://arxiv.org/abs/2502.04144,,,,"Derived benchmark suite built on HD-EPIC; tasks: Recipe, ingredient, nutrition, fine-grained action, 3D perception, object motion, gaze",benchmark; evaluation,
 EgoEnv,model,2022,project page,open,,2022,https://vision.cs.utexas.edu/projects/ego-env/,,,,Environment-aware representation learning from egocentric video,video-language; representation-learning,
+EgoMAS,model,2026-03,arXiv,open,,2026,https://ma-egoqa.github.io/,https://arxiv.org/abs/2603.09827,,,Shared-memory baseline for multi-agent egocentric video QA,video-language; representation-learning,
 Ego-Exo representation transfer,model,2021-04,CVPR 2021,open,,2021,https://arxiv.org/abs/2104.07905,,,,Distillation from third-person video using ego-specific latent signals,action; tracking; pose; hoi,
+EgoPoseFormer,model,2024-03,arXiv,open,,2024,https://github.com/ChenhongyiYang/egoposeformer,https://arxiv.org/abs/2403.18080,,,Transformer baseline for stereo egocentric 3D human pose estimation,action; tracking; pose; hoi,
 EgoSTARK,model,2023-01,arXiv,open,,2023,https://arxiv.org/abs/2301.03213,,,,Adapted long-term tracker baseline for EgoTracks,action; tracking; pose; hoi,
+EgoHOS model,model,2022-08,GitHub,open,,2022,https://github.com/owenzlz/EgoHOS,https://arxiv.org/abs/2208.03826,,,Context-aware hand-object segmentation and augmentation pipeline,action; tracking; pose; hoi,
+AV-CONV,model,2023-12,arXiv,open,,2023,https://vjwq.github.io/AV-CONV/,https://arxiv.org/abs/2312.12870,,,Audio-visual conversational graph prediction from ego/exo conversation,action; tracking; pose; hoi,
 EgoAction,model,2026-05,CVPR 2026,watch,,2026,https://arxiv.org/abs/2605.24496,,,,CVPR 2026 EPIC-KITCHENS action detection challenge pipeline,action; tracking; pose; hoi,
 EgoAdapt,model,2026-05,CVPR 2026,watch,,2026,https://arxiv.org/abs/2605.24500,,,,CVPR 2026 HD-EPIC VQA challenge inference-time adaptation pipeline,action; tracking; pose; hoi,
+Ego4D CLI and docs,toolkit,2021-10,project page,open,,2021,https://ego4d-data.org/,,,,Downloading and working with Ego4D data after license approval.,tooling,
+Ego-Exo4D CLI and docs,toolkit,2023-11,project page,open,,2023,https://ego-exo4d-data.org/,,,,Downloading synchronized ego-exo data and annotations.,tooling,
+VISOR API,toolkit,2022-09,GitHub,open,,2022,https://github.com/epic-kitchens/VISOR,,,,Loading dense EPIC-KITCHENS hand/object masks and relations.,tooling,
+EgoObjects API,toolkit,2023-09,arXiv,open,,2023,https://github.com/facebookresearch/EgoObjects,https://arxiv.org/abs/2309.08816,,,Working with category and instance-level egocentric object labels.,tooling,
+HOT3D tooling,toolkit,2024-06,project page,open,,,https://facebookresearch.github.io/hot3d/,,,,Loading HOT3D hand/object/camera pose annotations and models.,tooling,
+HOI4D tooling,toolkit,2022-03,project page,open,,2022,https://hoi4d.github.io/,,,,"Loading RGB-D frames, point clouds, object meshes, and pose/segmentation annotations.",tooling,
 EgoVid-5M,dataset,2024-11,NeurIPS 2025,open,,2024,https://egovid.github.io/,https://arxiv.org/abs/2411.08380,,not specified,5M curated egocentric clips at 1080p with fine-grained kinematic and high-level textual action annotations (NeurIPS 2025),video-generation; world-modeling; action-conditioned-generation,
 AoE,dataset,2026-02,arXiv,watch,,2026,https://arxiv.org/abs/2602.23893,,,,Always-on egocentric human video collection pipeline and corpus for embodied AI data scaling,robot-learning; data-collection; embodied-ai,
 Seeing Across Views (MV-RoboBench),benchmark,2025-10,ICLR 2026,open,adjacent,2025,https://github.com/microsoft/MV-RoboBench,https://arxiv.org/abs/2510.19400,,,MV-RoboBench: 1.7K curated QA items over eight subtasks testing multi-view spatial reasoning of VLMs in robotic manipulation scenes (ICLR 2026),multi-view; robot-manipulation; vlm-evaluation,
 EgoBrain,dataset,2025-06,arXiv,watch,,2025,https://arxiv.org/abs/2506.01353,,,,Synchronized EEG and egocentric video for human action understanding from minds and eyes,eeg; action-understanding; multimodal,
 MM-Ego,model,2024-10,ICLR 2025,open,,2024,https://arxiv.org/abs/2410.07177,,,,"Egocentric multimodal LLM with Memory Pointer Prompting; 7M-sample QA data engine and the EgoMemoria benchmark (629 videos, 7,026 questions)",video-language; memory; qa,
 EgoStream,benchmark,2026-05,arXiv,watch,,2026,https://arxiv.org/abs/2605.31557,,,,"2,250 questions across seven memory dimensions with Answer Validity Windows, expanded to 8,528 recall-conditioned evaluations over streams up to 45.3 hours",streaming-memory; episodic-memory; qa,
+EgoMemory,benchmark,2025-09,OpenReview,watch,,2025,https://openreview.net/forum?id=T0em4hJCQb,,,,"165,795 user-specific object annotations over 245 videos from 45 participants for memory-augmented personalized retrieval",personalized-retrieval; episodic-memory; long-context,
+EgoTextVQA,benchmark,2025-06,CVPR 2025,open,,2025,https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_EgoTextVQA_Towards_Egocentric_Scene-Text_Aware_Video_Question_Answering_CVPR_2025_paper.pdf,,,,Egocentric scene-text-aware video QA across indoor housekeeping and outdoor driving scenes (CVPR 2025),scene-text; qa; reading,
 Gesture-Based Egocentric Video QA,benchmark,2026-03,CVPR 2026,watch,,2026,https://arxiv.org/abs/2603.12533,,,,Egocentric video QA grounded in the camera wearer's pointing and deictic gestures,gesture-grounding; qa; referential,
+EgoVQA,benchmark,2019-10,ICCV 2019,open,,2019,https://openaccess.thecvf.com/content_ICCVW_2019/html/EPIC/Fan_EgoVQA_-_An_Egocentric_Video_Question_Answering_Benchmark_Dataset_ICCVW_2019_paper.html,,,,600+ QA pairs over egocentric videos; an early first-person video question answering benchmark,qa; action; classic,
 ExAct,benchmark,2025-06,arXiv,open,,2025,https://arxiv.org/abs/2506.06277,,,,Video-language benchmark for expert action analysis and feedback over skilled egocentric/exocentric activity,expert-feedback; skill-assessment; video-language,
 Home Action Genome / HOMAGE,dataset,2021-05,CVPR 2021,open,,2021,https://homeactiongenome.org/,https://arxiv.org/abs/2105.05226,,not specified,"27 participants, multi-modal synchronized ego and third-person views with 12 sensor types and hierarchical activity/action labels in home settings",action-recognition; multi-view; compositional,
 EgoExo-Fitness,dataset,2024-06,ECCV 2024,open,,2024,https://github.com/iSEE-Laboratory/EgoExo-Fitness,https://arxiv.org/abs/2406.08877,,Apache-2.0,"Synchronized ego and exo fitness videos with keypoint verification, execution comments, and action quality scores (ECCV 2024)",ego-exo; action-quality; skill-assessment,
 EgoMotion,model,2026-04,arXiv,watch,,2026,https://arxiv.org/abs/2604.19105,https://arxiv.org/abs/2604.19105,,,Hierarchical reasoning plus diffusion framework for egocentric vision-language motion generation from first-person context,motion; motion-estimation; video-language,egocentric-video; motion; text
 Gaze-SoM HOI Anticipation,model,2026-04,ICPR 2026,watch,,2026,https://arxiv.org/abs/2604.03667,https://arxiv.org/abs/2604.03667,,,Leverages gaze and set-of-mark prompting in vision-language LLMs for hand-object-interaction anticipation from egocentric video,anticipation; hand-object; gaze-reasoning,egocentric-video; gaze; text
 Sanpo-D,benchmark,2026-01,arXiv,watch,,2026,https://arxiv.org/abs/2601.18100,https://arxiv.org/abs/2601.18100,,,"Fine-grained re-annotation of the Sanpo egocentric navigation data with spatial signals, benchmarking spatial-conditioned reasoning of VLMs over long first-person videos",spatial-reasoning; navigation; video-qa,egocentric-video; qa
+EgoSurgery,dataset,2025-03,arXiv,open,,2025,https://github.com/Fujiry0/EgoSurgery,https://arxiv.org/abs/2503.18755,https://github.com/Fujiry0/EgoSurgery,custom,Egocentric open-surgery video dataset family: EgoSurgery-Phase for surgical phase recognition and EgoSurgery-HTS for pixel-wise hand-tool segmentation of 14 surgical tools (MICCAI 2024),action-recognition; hand-object-segmentation; procedural-task-assistance,egocentric-video; segmentation; object-boxes
 EgoEMS,dataset,2025-11,AAAI 2026,watch,,2025,https://arxiv.org/abs/2511.09894,https://arxiv.org/abs/2511.09894,,,"High-fidelity multimodal egocentric dataset for cognitive assistance in emergency medical services, capturing time-critical team actions in high-stakes scenarios",procedural-task-assistance; action-recognition; real-time-understanding,egocentric-video; audio; multimodal-annotations
 LEMMA,dataset,2020-07,ECCV 2020,open,,2020,https://arxiv.org/abs/2007.15781,https://arxiv.org/abs/2007.15781,,not specified,"Multi-view multi-agent multi-task daily-activity dataset across 14 kitchens and living rooms, densely annotated with atomic actions and human-object interactions (ECCV 2020)",compositional; action-recognition; anticipation,egocentric-video; third-person-video; multimodal-annotations
 EgoMe,dataset,2025-01,arXiv,watch,,2025,https://arxiv.org/abs/2501.19061,https://arxiv.org/abs/2501.19061,,,"Real-world dataset and challenge for following a demonstrator via egocentric view, pairing exocentric demonstrations with egocentric imitation across everyday tasks",imitation-learning; cross-view; action-understanding,egocentric-video; exocentric-video
 3D-Aware Ego Instance Tracking,model,2024-08,arXiv,watch,,2024,https://arxiv.org/abs/2408.09860,https://arxiv.org/abs/2408.09860,,,3D-aware instance segmentation and multi-object tracking in egocentric video that lifts 2D masks with scene geometry to survive rapid motion and occlusion (large ID-switch reductions on EPIC Fields),detection-segmentation; tracking; three-d-and-scene,egocentric-video; camera-pose
 EgoCogNav,model,2025-11,arXiv,watch,,2025,https://arxiv.org/abs/2511.17581,https://arxiv.org/abs/2511.17581,,,"Cognition-aware egocentric navigation framework that forecasts human trajectory and head motion from perceived path uncertainty, modeling scanning, hesitation, and backtracking, with the 6-hour CEN dataset",ar-sensing-navigation; reasoning-intent-planning; pose-and-body,egocentric-video; motion
 Neck-Mounted Gaze (GLC),model,2026-02,arXiv,watch,,2026,https://arxiv.org/abs/2602.11669,https://arxiv.org/abs/2602.11669,,,"Transformer gaze estimator (GLC) for a shoulder-level neck-mounted camera with an out-of-bound gaze classification task and head/neck multi-view co-learning, plus a ~4-hour 8-subject dataset",ar-sensing-navigation; pose-and-body,egocentric-video; gaze
+CMU-MMAC,dataset,2009-06,CMU tech report 2009,open,,2009,http://kitchen.cs.cmu.edu/,http://kitchen.cs.cmu.edu/,,not specified,"Among the earliest egocentric datasets: a multimodal kitchen-activity database with head-mounted egocentric video plus body IMUs, motion capture, and audio for 43 subjects cooking 5 recipes",action-and-procedure; detection-segmentation,egocentric-video; imu; motion; audio
+First-Person Social Interactions,dataset,2012-06,CVPR 2012,open,,2012,http://ai.stanford.edu/~alireza/publication/CVPR12.pdf,http://ai.stanford.edu/~alireza/publication/CVPR12.pdf,,not specified,"The first egocentric social-interaction dataset: day-long head-mounted video of 8 subjects at a theme park, annotated for social interactions, roles, attention, and turn-taking",audio-and-social; reasoning-intent-planning; detection-segmentation,egocentric-video
+BEOID,dataset,2014-09,BMVC 2014,open,,2014,https://dimadamen.github.io/BEOID/,https://dimadamen.github.io/BEOID/,,not specified,"Gaze-tracked egocentric video of 8 users interacting with objects across 6 everyday locations (kitchen, workspace, printer, corridor, gym) for discovering task-relevant objects and interaction modes",hand-object-interaction; action-and-procedure; detection-segmentation,egocentric-video; gaze
 DreamDojo,model,2026-02,ICML 2026,open,,2026,https://github.com/NVIDIA/DreamDojo,https://arxiv.org/abs/2602.06949,https://github.com/NVIDIA/DreamDojo,,"Generalist robot world model pretrained on 44K hours of egocentric human video using continuous latent actions; distilled to real-time (10.8 FPS) for teleoperation, policy evaluation, and model-based planning",world-modeling; robot-learning; video-generation,egocentric-video; latent-actions
 UniDex,model,2026-03,CVPR 2026,watch,,2026,https://arxiv.org/abs/2603.22264,https://arxiv.org/abs/2603.22264,,,"Robot foundation suite for universal dexterous hand control learned from egocentric human videos, transferring human manipulation priors to multi-fingered robot hands",robot-learning; dexterous-manipulation; vla,egocentric-video; hand-pose
 DexGloveHOI,dataset,2026-05,arXiv,watch,,2026,https://arxiv.org/abs/2605.21714,https://arxiv.org/abs/2605.21714,,,100K+ synchronized egocentric vision and on-glove IMU samples with marker-based mocap ground-truth 3D hand pose across dexterous daily object manipulation (introduced with the AVI-HT vision-IMU fusion method),pose-and-body; hand-object-interaction; 3d-4d-understanding,egocentric-video; imu; hand-pose
 First-Person Activity Forecasting,model,2016-12,ICCV 2017 Oral,open,,2016,https://arxiv.org/abs/1612.07796,https://arxiv.org/abs/1605.09070,https://darko.cs.utexas.edu/,,DARKO online inverse-reinforcement-learning system for forecasting actions and active objects in first-person video,anticipation; action-recognition; active-object-detection,egocentric-video; object-boxes
 Ego-Object Discovery / EDUB,dataset,2015-04,arXiv,open,,2015,https://arxiv.org/abs/1504.01639,https://arxiv.org/abs/1504.01639,,unknown-public,"EDUB egocentric photo-stream dataset for unsupervised object discovery, with 4,912 daily-life images from four users and public code/data reported by the paper",object-discovery; detection-segmentation; lifelogging,egocentric-images; object-boxes
 Egocentric FOV Localization,model,2015-01,WACV 2015,watch,,2015,https://arxiv.org/abs/1501.01643,https://arxiv.org/abs/1501.01643,,,Localizes the first-person camera wearer's field of view in surveillance/top-view video using wearable point-of-view devices,fov-localization; cross-view; person-localization,egocentric-video; overhead-video
+First-Person Pose Recognition,model,2015-06,CVPR 2015,watch,,2015,https://openaccess.thecvf.com/content_cvpr_2015/html/Rogez_First-Person_Pose_Recognition_2015_CVPR_paper.html,https://openaccess.thecvf.com/content_cvpr_2015/html/Rogez_First-Person_Pose_Recognition_2015_CVPR_paper.html,,,Egocentric workspace method for recognizing human pose from first-person camera geometry and likely body configurations,pose-and-body; egocentric-3d-pose; wearable-sensing,egocentric-video; body-pose

data/resources.yml CHANGED Viewed

@@ -1,6 +1,6 @@
 meta:
   title: Awesome Egocentric Atlas
-  description: "Just launched Awesome Egocentric Atlas — a curated collection of first-person AI resources for egocentric vision, embodied AI, robotics, VLA, world models, WMA, memory, AR/VR, and hand-object interaction."
   last_major_audit: "2026-06-20"
   scope: Egocentric, first-person, wearable-camera, AR/VR headset, body/wrist camera, and ego-exo datasets, benchmarks, models, and tools.
   status_legend:
@@ -14,7 +14,7 @@ resources:
   - name: Xperience-10M
     verified_at: "2026-06-20"
     milestone: "2026-03"
-    milestone_note: "Petascale egocentric world-model corpus (10M experiences, ~1 PB) pushing first-person data to internet scale for embodied AI and robot learning."
     milestone_image: assets/milestones/xperience-10m.png
     kind: dataset
     released: "2026-03"
@@ -33,7 +33,7 @@ resources:
   - name: Xperience-10M Sample
     kind: dataset
-    released: "2026"
     venue: "Hugging Face"
     year: 2026
     status: open
@@ -486,7 +486,7 @@ resources:
     verified_at: "2026-06-20"
   - name: EgoHands
     kind: dataset
-    released: "2015"
     venue: "ICCV 2015"
     year: 2015
     status: open
@@ -497,17 +497,18 @@ resources:
     license: "not specified"
     license_url: "http://vision.soic.indiana.edu/projects/egohands/"
     verified_at: "2026-06-20"
-    milestone: "2015"
     milestone_note: "The Google Glass hand-segmentation benchmark that made hands a first-class signal for egocentric perception."
     milestone_image: assets/milestones/egohands.png
   - name: FPHA
     kind: dataset
-    released: "2017"
     venue: "CVPR 2018"
     year: 2017
     status: open
     url: https://guiggh.github.io/publications/first-person-hands/
     scale: "100K+ RGB-D frames, 45 action classes, 26 objects"
     modalities: [rgbd, 3d-hand-pose, object-pose]
     tasks: [hand-action-recognition, 3d-hand-pose]
@@ -556,11 +557,12 @@ resources:
     verified_at: "2026-06-20"
   - name: EgoBody
     kind: dataset
-    released: "2022"
     venue: "ECCV 2022"
-    year: 2022
     status: open
     url: https://sanweiliti.github.io/egobody/egobody.html
     modalities: [hololens2, rgb, depth, gaze, head-pose, hand-pose, body-pose]
     tasks: [3d-human-pose, body-shape, motion]
     license: "not specified"
@@ -607,11 +609,12 @@ resources:
     verified_at: "2026-06-20"
   - name: xR-EgoPose
     kind: dataset
-    released: "2019"
     venue: "GitHub"
     year: 2019
     status: open
     url: https://github.com/facebookresearch/xR-EgoPose
     modalities: [synthetic-egocentric, 3d-human-pose]
     tasks: [xr-pose-estimation]
     license: "custom"
@@ -642,25 +645,27 @@ resources:
     verified_at: "2026-06-20"
   - name: TREK-150
     kind: benchmark
-    released: "2021"
     venue: "project page"
     year: 2021
     status: open
     url: https://machinelearning.uniud.it/datasets/trek150/
     derived_from: EPIC-KITCHENS
     tasks: [single-object-tracking]
     verified_at: "2026-06-20"
   - name: Project Aria Datasets
     verified_at: "2026-06-20"
-    milestone: "2022"
     milestone_note: "Meta's research smart-glasses platform that opened the modern wave of AR and wearable egocentric data."
     milestone_image: assets/milestones/project-aria.png
     kind: collection
-    released: "2022"
-    venue: "project page"
-    year: 2022
     status: open
     url: https://www.projectaria.com/datasets/
     modalities: [aria, vrs, calibration, gaze, imu, mps]
     tasks: [ar-perception, scene-understanding, wearable-sensing]
@@ -1090,7 +1095,7 @@ resources:
     license_url: "https://cbs.ic.gatech.edu/fpv/"
     verified_at: "2026-06-20"
     milestone: "2011-06"
-    milestone_note: "Foundational hand-object and gaze egocentric activity datasets (GTEA, CVPR 2011) that seeded first-person action and attention research."
     milestone_image: assets/milestones/gtea-gaze.png
     kind: dataset
     released: "2018"
@@ -1221,11 +1226,12 @@ resources:
     verified_at: "2026-06-20"
   - name: VISOR
     kind: benchmark
-    released: "2022"
     venue: "NeurIPS 2022"
     year: 2022
     status: open
     url: https://epic-kitchens.github.io/VISOR/
     derived_from: EPIC-KITCHENS
     license: cc-by-nc-4.0
     verified_at: "2026-06-15"
@@ -1233,21 +1239,23 @@ resources:
   - name: EPIC-Sounds
     kind: benchmark
-    released: "2023"
     venue: "ICASSP 2023"
     year: 2023
     status: open
     url: https://epic-kitchens.github.io/epic-sounds/
     derived_from: EPIC-KITCHENS
     tasks: [audio-event-recognition]
     verified_at: "2026-06-20"
   - name: EPIC-Fields
     kind: benchmark
-    released: "2023"
     venue: "NeurIPS 2023"
     year: 2023
     status: open
     url: https://epic-kitchens.github.io/epic-fields/
     derived_from: EPIC-KITCHENS
     tasks: [3d-fields, spatial-reasoning]
     verified_at: "2026-06-20"
@@ -1329,7 +1337,7 @@ resources:
     release_note: "Live source checked 2026-06-20; kept on watch until public artifacts, access terms, or reproducible release metadata are confirmed."
   - name: Ropedia Xperience-10M Task Suite
     kind: benchmark
-    released: "2026"
     venue: "Hugging Face"
     year: 2026
     status: open
@@ -1344,7 +1352,7 @@ resources:
     verified_at: "2026-06-20"
   - name: Ropedia Xperience-10M Task Baselines
     kind: model
-    released: "2026"
     venue: "Hugging Face"
     year: 2026
     status: open
@@ -1422,11 +1430,12 @@ resources:
     verified_at: "2026-06-20"
   - name: Project Aria Tools
     kind: toolkit
-    released: "2022"
-    venue: "GitHub"
-    year: 2022
     status: open
     url: https://github.com/facebookresearch/projectaria_tools
     tasks: [vrs-loading, calibration, mps, gaze, trajectory]
     verified_at: "2026-06-20"
   - name: EPIC-KITCHENS action models
@@ -1440,7 +1449,7 @@ resources:
     verified_at: "2026-06-20"
   - name: HOMIE-toolkit
     kind: toolkit
-    released: "2026"
     venue: "GitHub"
     year: 2026
     status: open
@@ -2087,11 +2096,12 @@ resources:
   - name: EgoCom / Ego audio-visual correspondence
     kind: dataset
-    released: "2020"
-    venue: "project page"
-    year: 2020
     status: open
     url: http://vision.cs.utexas.edu/projects/ego_av_corr/
     scale: "Egocentric video with spatial audio for conversation and audio-visual correspondence tasks"
     tasks: [foundation-video, video-language]
     license: "not specified"
@@ -2127,7 +2137,7 @@ resources:
     milestone_note: "Showed vision-language-action policies can be learned from egocentric human video and transferred to robots."
     milestone_image: assets/milestones/egovla.png
     kind: model
-    released: "2025"
     venue: "project page"
     status: watch
     url: https://rchalyang.github.io/EgoVLA/
@@ -2214,14 +2224,16 @@ resources:
     release_note: "Live source checked 2026-06-20; kept on watch until public artifacts, access terms, or reproducible release metadata are confirmed."
   - name: EgoEVHands
     kind: dataset
-    released: "2024"
-    venue: "GitHub"
     status: watch
     url: https://github.com/ZJUWang01/EgoEV-HandPose
     scale: "Stereo event-camera egocentric hand dataset with 5,419 sequences and 3D/2D keypoints"
     tasks: [hand-object, 3d, pose]
     verified_at: "2026-06-20"
-    release_note: "Live source checked 2026-06-20; kept on watch until public artifacts, access terms, or reproducible release metadata are confirmed."
   - name: EventEgoHands
     kind: benchmark
     released: "2025-05"
@@ -2257,27 +2269,31 @@ resources:
     release_note: "Live source checked 2026-06-20; kept on watch until public artifacts, access terms, or reproducible release metadata are confirmed."
   - name: EgoSelf
     kind: model
-    released: "2025"
-    venue: "project page"
     status: watch
     url: https://abie-e.github.io/egoself_project/
     scale: "Personalized egocentric assistant framework with graph memory"
     tasks: [memory, qa, assistant]
     verified_at: "2026-06-20"
     release_note: "Live access check failed 2026-06-20; kept on watch until the source page reappears or release artifacts are confirmed."
   - name: EgoCross
     kind: dataset
-    released: "2025"
-    venue: "GitHub"
     status: watch
     url: https://github.com/MyUniverse0726/EgoCross
     scale: "About 1,000 QA pairs over surgery, industry, extreme sports, and animal-perspective clips"
     tasks: [memory, qa, assistant]
     verified_at: "2026-06-20"
-    release_note: "Live source checked 2026-06-20; kept on watch until public artifacts, access terms, or reproducible release metadata are confirmed."
   - name: ADL Dataset
     kind: dataset
-    released: "2012"
     venue: "project page"
     year: 2012
     status: partial
@@ -2286,8 +2302,8 @@ resources:
     tasks: [action-recognition, procedure]
     verified_at: "2026-06-20"
     release_note: "Live source checked 2026-06-20; catalog remains partial because access, annotations, or terms are incomplete, gated, or not fully verified."
-    milestone: "2012"
-    milestone_note: "Classic unscripted daily-life egocentric activity corpus with object and hand annotations; the reference point for ADL-scale first-person recognition."
     milestone_image: assets/milestones/adl-dataset.png
   - name: Wrist-mounted ADL
@@ -2304,18 +2320,19 @@ resources:
     verified_at: "2026-06-20"
   - name: Visual Experience Dataset / VEDB
     kind: dataset
-    released: "2023"
-    venue: "project page"
-    year: 2023
     status: partial
     url: http://tamaraberg.com/visualexperience/
     scale: "240+ hours egocentric video with gaze/head tracking in classic literature"
     tasks: [action-recognition, procedure]
     verified_at: "2026-06-20"
     release_note: "Live access check failed 2026-06-20; catalog remains partial pending a working source or confirmed mirror."
   - name: UT Ego
     kind: dataset
-    released: "2012"
     venue: "project page"
     year: 2012
     status: partial
@@ -2326,7 +2343,7 @@ resources:
     release_note: "Live source checked 2026-06-20; catalog remains partial because access, annotations, or terms are incomplete, gated, or not fully verified."
   - name: HUJI EgoSeg
     kind: dataset
-    released: "2014"
     venue: "project page"
     year: 2014
     status: partial
@@ -2348,11 +2365,12 @@ resources:
     release_note: "Live source checked 2026-06-20; catalog remains partial because access, annotations, or terms are incomplete, gated, or not fully verified."
   - name: FT-HID
     kind: dataset
-    released: "2022"
     venue: "GitHub"
     year: 2022
     status: open
     url: https://github.com/ENDLICHERE/FT-HID
     scale: "90K+ RGB-D first- and third-person human interaction samples from 109 subjects"
     tasks: [action-recognition, procedure]
     license: "not specified"
@@ -2384,7 +2402,7 @@ resources:
   - name: Ego4D Benchmarks
     kind: benchmark
-    released: "2021"
     venue: "project page"
     year: 2021
     status: benchmark
@@ -2394,7 +2412,7 @@ resources:
     verified_at: "2026-06-20"
   - name: Ego-Exo4D Benchmarks
     kind: benchmark
-    released: "2023"
     venue: "project page"
     year: 2023
     status: benchmark
@@ -2404,7 +2422,7 @@ resources:
     verified_at: "2026-06-20"
   - name: EPIC-KITCHENS Challenges
     kind: benchmark
-    released: "2018"
     venue: "project page"
     year: 2018
     status: benchmark
@@ -2434,10 +2452,12 @@ resources:
     verified_at: "2026-06-20"
   - name: EgoMAS
     kind: model
-    released: "2026"
-    venue: "project page"
     status: open
     url: https://ma-egoqa.github.io/
     scale: "Shared-memory baseline for multi-agent egocentric video QA"
     tasks: [video-language, representation-learning]
     verified_at: "2026-06-20"
@@ -2453,10 +2473,12 @@ resources:
     verified_at: "2026-06-20"
   - name: EgoPoseFormer
     kind: model
-    released: "2024"
-    venue: "GitHub"
     status: open
     url: https://github.com/ChenhongyiYang/egoposeformer
     scale: "Transformer baseline for stereo egocentric 3D human pose estimation"
     tasks: [action, tracking, pose, hoi]
     verified_at: "2026-06-20"
@@ -2472,21 +2494,23 @@ resources:
     verified_at: "2026-06-20"
   - name: EgoHOS model
     kind: model
-    released: "2022"
     venue: "GitHub"
     year: 2022
     status: open
     url: https://github.com/owenzlz/EgoHOS
     scale: "Context-aware hand-object segmentation and augmentation pipeline"
     tasks: [action, tracking, pose, hoi]
     verified_at: "2026-06-20"
   - name: AV-CONV
     kind: model
-    released: "2023"
-    venue: "project page"
     year: 2023
     status: open
     url: https://vjwq.github.io/AV-CONV/
     scale: "Audio-visual conversational graph prediction from ego/exo conversation"
     tasks: [action, tracking, pose, hoi]
     verified_at: "2026-06-20"
@@ -2514,7 +2538,7 @@ resources:
     release_note: "Live source checked 2026-06-20; kept on watch until public artifacts, access terms, or reproducible release metadata are confirmed."
   - name: Ego4D CLI and docs
     kind: toolkit
-    released: "2021"
     venue: "project page"
     year: 2021
     status: open
@@ -2524,7 +2548,7 @@ resources:
     verified_at: "2026-06-20"
   - name: Ego-Exo4D CLI and docs
     kind: toolkit
-    released: "2023"
     venue: "project page"
     year: 2023
     status: open
@@ -2534,7 +2558,7 @@ resources:
     verified_at: "2026-06-20"
   - name: VISOR API
     kind: toolkit
-    released: "2022"
     venue: "GitHub"
     year: 2022
     status: open
@@ -2544,17 +2568,18 @@ resources:
     verified_at: "2026-06-20"
   - name: EgoObjects API
     kind: toolkit
-    released: "2023"
-    venue: "GitHub"
     year: 2023
     status: open
     url: https://github.com/facebookresearch/EgoObjects
     scale: "Working with category and instance-level egocentric object labels."
     tasks: [tooling]
     verified_at: "2026-06-20"
   - name: HOT3D tooling
     kind: toolkit
-    released: "2024"
     venue: "project page"
     status: open
     url: https://facebookresearch.github.io/hot3d/
@@ -2563,7 +2588,7 @@ resources:
     verified_at: "2026-06-20"
   - name: HOI4D tooling
     kind: toolkit
-    released: "2022"
     venue: "project page"
     year: 2022
     status: open
@@ -2655,9 +2680,9 @@ resources:
     release_note: "Live source checked 2026-06-20; kept on watch until public artifacts, access terms, or reproducible release metadata are confirmed."
   - name: EgoMemory
     kind: benchmark
-    released: "2026"
     venue: "OpenReview"
-    year: 2026
     status: watch
     url: https://openreview.net/forum?id=T0em4hJCQb
     scale: "165,795 user-specific object annotations over 245 videos from 45 participants for memory-augmented personalized retrieval"
@@ -2666,7 +2691,7 @@ resources:
     release_note: "Live source checked 2026-06-20; kept on watch until public artifacts, access terms, or reproducible release metadata are confirmed."
   - name: EgoTextVQA
     kind: benchmark
-    released: "2025"
     venue: "CVPR 2025"
     year: 2025
     status: open
@@ -2687,7 +2712,7 @@ resources:
     release_note: "Live source checked 2026-06-20; kept on watch until public artifacts, access terms, or reproducible release metadata are confirmed."
   - name: EgoVQA
     kind: benchmark
-    released: "2019"
     venue: "ICCV 2019"
     year: 2019
     status: open
@@ -3232,9 +3257,9 @@ resources:
   - name: EgoSurgery
     kind: dataset
-    year: 2024
-    released: "2024"
-    venue: "MICCAI 2024"
     status: open
     url: https://github.com/Fujiry0/EgoSurgery
     paper: https://arxiv.org/abs/2503.18755
@@ -4294,11 +4319,11 @@ resources:
   - name: CMU-MMAC
     license: "not specified"
     license_url: "http://kitchen.cs.cmu.edu/"
-    milestone: "2009"
-    milestone_note: "The earliest egocentric dataset; launched first-person activity recognition at the first IEEE Workshop on Egocentric Vision (CVPR 2009)."
     milestone_image: assets/milestones/cmu-mmac.png
     kind: dataset
-    released: "2009"
     venue: "CMU tech report 2009"
     year: 2009
     status: open
@@ -4313,7 +4338,7 @@ resources:
   - name: First-Person Social Interactions
     kind: dataset
-    released: "2012"
     venue: "CVPR 2012"
     year: 2012
     status: open
@@ -4329,7 +4354,7 @@ resources:
   - name: BEOID
     kind: dataset
-    released: "2014"
     venue: "BMVC 2014"
     year: 2014
     status: open
@@ -4356,6 +4381,9 @@ resources:
     modalities: [egocentric-video, latent-actions]
     tasks: [world-modeling, robot-learning, video-generation]
     verified_at: "2026-06-20"
     citation_key: dreamdojo_2026
   - name: UniDex
@@ -6480,7 +6508,7 @@ resources:
   - name: First-Person Pose Recognition
     kind: model
-    released: "2015"
     venue: "CVPR 2015"
     year: 2015
     status: watch

 meta:
   title: Awesome Egocentric Atlas
+  description: "Just launched Awesome Egocentric Atlas — a curated collection of egocentric AI resources for egocentric vision, embodied AI, robotics, VLA, world models, WMA, memory, AR/VR, and hand-object interaction."
   last_major_audit: "2026-06-20"
   scope: Egocentric, first-person, wearable-camera, AR/VR headset, body/wrist camera, and ego-exo datasets, benchmarks, models, and tools.
   status_legend:
   - name: Xperience-10M
     verified_at: "2026-06-20"
     milestone: "2026-03"
+    milestone_note: "Petascale egocentric world-model corpus (10M experiences, ~1 PB) pushing egocentric data to internet scale for embodied AI and robot learning."
     milestone_image: assets/milestones/xperience-10m.png
     kind: dataset
     released: "2026-03"
   - name: Xperience-10M Sample
     kind: dataset
+    released: "2026-03"
     venue: "Hugging Face"
     year: 2026
     status: open
     verified_at: "2026-06-20"
   - name: EgoHands
     kind: dataset
+    released: "2015-12"
     venue: "ICCV 2015"
     year: 2015
     status: open
     license: "not specified"
     license_url: "http://vision.soic.indiana.edu/projects/egohands/"
     verified_at: "2026-06-20"
+    milestone: "2015-12"
     milestone_note: "The Google Glass hand-segmentation benchmark that made hands a first-class signal for egocentric perception."
     milestone_image: assets/milestones/egohands.png
   - name: FPHA
     kind: dataset
+    released: "2017-04"
     venue: "CVPR 2018"
     year: 2017
     status: open
     url: https://guiggh.github.io/publications/first-person-hands/
+    paper: https://arxiv.org/abs/1704.02463
     scale: "100K+ RGB-D frames, 45 action classes, 26 objects"
     modalities: [rgbd, 3d-hand-pose, object-pose]
     tasks: [hand-action-recognition, 3d-hand-pose]
     verified_at: "2026-06-20"
   - name: EgoBody
     kind: dataset
+    released: "2021-12"
     venue: "ECCV 2022"
+    year: 2021
     status: open
     url: https://sanweiliti.github.io/egobody/egobody.html
+    paper: https://arxiv.org/abs/2112.07642
     modalities: [hololens2, rgb, depth, gaze, head-pose, hand-pose, body-pose]
     tasks: [3d-human-pose, body-shape, motion]
     license: "not specified"
     verified_at: "2026-06-20"
   - name: xR-EgoPose
     kind: dataset
+    released: "2019-07"
     venue: "GitHub"
     year: 2019
     status: open
     url: https://github.com/facebookresearch/xR-EgoPose
+    paper: https://arxiv.org/abs/1907.10045
     modalities: [synthetic-egocentric, 3d-human-pose]
     tasks: [xr-pose-estimation]
     license: "custom"
     verified_at: "2026-06-20"
   - name: TREK-150
     kind: benchmark
+    released: "2021-08"
     venue: "project page"
     year: 2021
     status: open
     url: https://machinelearning.uniud.it/datasets/trek150/
+    paper: https://arxiv.org/abs/2108.13665
     derived_from: EPIC-KITCHENS
     tasks: [single-object-tracking]
     verified_at: "2026-06-20"
   - name: Project Aria Datasets
     verified_at: "2026-06-20"
+    milestone: "2023-08"
     milestone_note: "Meta's research smart-glasses platform that opened the modern wave of AR and wearable egocentric data."
     milestone_image: assets/milestones/project-aria.png
     kind: collection
+    released: "2023-08"
+    venue: "arXiv"
+    year: 2023
     status: open
     url: https://www.projectaria.com/datasets/
+    paper: https://arxiv.org/abs/2308.13561
     modalities: [aria, vrs, calibration, gaze, imu, mps]
     tasks: [ar-perception, scene-understanding, wearable-sensing]
     license_url: "https://cbs.ic.gatech.edu/fpv/"
     verified_at: "2026-06-20"
     milestone: "2011-06"
+    milestone_note: "Foundational hand-object and gaze egocentric activity datasets (GTEA, CVPR 2011) that seeded egocentric action and attention research."
     milestone_image: assets/milestones/gtea-gaze.png
     kind: dataset
     released: "2018"
     verified_at: "2026-06-20"
   - name: VISOR
     kind: benchmark
+    released: "2022-09"
     venue: "NeurIPS 2022"
     year: 2022
     status: open
     url: https://epic-kitchens.github.io/VISOR/
+    paper: https://arxiv.org/abs/2209.08199
     derived_from: EPIC-KITCHENS
     license: cc-by-nc-4.0
     verified_at: "2026-06-15"
   - name: EPIC-Sounds
     kind: benchmark
+    released: "2023-02"
     venue: "ICASSP 2023"
     year: 2023
     status: open
     url: https://epic-kitchens.github.io/epic-sounds/
+    paper: https://arxiv.org/abs/2302.00646
     derived_from: EPIC-KITCHENS
     tasks: [audio-event-recognition]
     verified_at: "2026-06-20"
   - name: EPIC-Fields
     kind: benchmark
+    released: "2023-06"
     venue: "NeurIPS 2023"
     year: 2023
     status: open
     url: https://epic-kitchens.github.io/epic-fields/
+    paper: https://arxiv.org/abs/2306.08731
     derived_from: EPIC-KITCHENS
     tasks: [3d-fields, spatial-reasoning]
     verified_at: "2026-06-20"
     release_note: "Live source checked 2026-06-20; kept on watch until public artifacts, access terms, or reproducible release metadata are confirmed."
   - name: Ropedia Xperience-10M Task Suite
     kind: benchmark
+    released: "2026-03"
     venue: "Hugging Face"
     year: 2026
     status: open
     verified_at: "2026-06-20"
   - name: Ropedia Xperience-10M Task Baselines
     kind: model
+    released: "2026-03"
     venue: "Hugging Face"
     year: 2026
     status: open
     verified_at: "2026-06-20"
   - name: Project Aria Tools
     kind: toolkit
+    released: "2023-08"
+    venue: "arXiv"
+    year: 2023
     status: open
     url: https://github.com/facebookresearch/projectaria_tools
+    paper: https://arxiv.org/abs/2308.13561
     tasks: [vrs-loading, calibration, mps, gaze, trajectory]
     verified_at: "2026-06-20"
   - name: EPIC-KITCHENS action models
     verified_at: "2026-06-20"
   - name: HOMIE-toolkit
     kind: toolkit
+    released: "2026-03"
     venue: "GitHub"
     year: 2026
     status: open
   - name: EgoCom / Ego audio-visual correspondence
     kind: dataset
+    released: "2023-07"
+    venue: "arXiv"
+    year: 2023
     status: open
     url: http://vision.cs.utexas.edu/projects/ego_av_corr/
+    paper: https://arxiv.org/abs/2307.04760
     scale: "Egocentric video with spatial audio for conversation and audio-visual correspondence tasks"
     tasks: [foundation-video, video-language]
     license: "not specified"
     milestone_note: "Showed vision-language-action policies can be learned from egocentric human video and transferred to robots."
     milestone_image: assets/milestones/egovla.png
     kind: model
+    released: "2025-07"
     venue: "project page"
     status: watch
     url: https://rchalyang.github.io/EgoVLA/
     release_note: "Live source checked 2026-06-20; kept on watch until public artifacts, access terms, or reproducible release metadata are confirmed."
   - name: EgoEVHands
     kind: dataset
+    released: "2026-05"
+    venue: "arXiv"
+    year: 2026
     status: watch
     url: https://github.com/ZJUWang01/EgoEV-HandPose
+    paper: https://arxiv.org/abs/2605.12297
     scale: "Stereo event-camera egocentric hand dataset with 5,419 sequences and 3D/2D keypoints"
     tasks: [hand-object, 3d, pose]
     verified_at: "2026-06-20"
+    release_note: "GitHub README rechecked 2026-06-20; source code, pretrained models, and EgoEVHands dataset release are still marked pending."
   - name: EventEgoHands
     kind: benchmark
     released: "2025-05"
     release_note: "Live source checked 2026-06-20; kept on watch until public artifacts, access terms, or reproducible release metadata are confirmed."
   - name: EgoSelf
     kind: model
+    released: "2026-04"
+    venue: "arXiv"
+    year: 2026
     status: watch
     url: https://abie-e.github.io/egoself_project/
+    paper: https://arxiv.org/abs/2604.19564
     scale: "Personalized egocentric assistant framework with graph memory"
     tasks: [memory, qa, assistant]
     verified_at: "2026-06-20"
     release_note: "Live access check failed 2026-06-20; kept on watch until the source page reappears or release artifacts are confirmed."
   - name: EgoCross
     kind: dataset
+    released: "2025-08"
+    venue: "arXiv"
+    year: 2025
     status: watch
     url: https://github.com/MyUniverse0726/EgoCross
+    paper: https://arxiv.org/abs/2508.10729
     scale: "About 1,000 QA pairs over surgery, industry, extreme sports, and animal-perspective clips"
     tasks: [memory, qa, assistant]
     verified_at: "2026-06-20"
+    release_note: "GitHub README rechecked 2026-06-20; evaluator code is public, but benchmark JSON/data files are expected under datasets/ and are not bundled with clear access terms."
   - name: ADL Dataset
     kind: dataset
+    released: "2012-06"
     venue: "project page"
     year: 2012
     status: partial
     tasks: [action-recognition, procedure]
     verified_at: "2026-06-20"
     release_note: "Live source checked 2026-06-20; catalog remains partial because access, annotations, or terms are incomplete, gated, or not fully verified."
+    milestone: "2012-06"
+    milestone_note: "Classic unscripted daily-life egocentric activity corpus with object and hand annotations; the reference point for ADL-scale egocentric recognition."
     milestone_image: assets/milestones/adl-dataset.png
   - name: Wrist-mounted ADL
     verified_at: "2026-06-20"
   - name: Visual Experience Dataset / VEDB
     kind: dataset
+    released: "2024-02"
+    venue: "arXiv"
+    year: 2024
     status: partial
     url: http://tamaraberg.com/visualexperience/
+    paper: https://arxiv.org/abs/2404.18934
     scale: "240+ hours egocentric video with gaze/head tracking in classic literature"
     tasks: [action-recognition, procedure]
     verified_at: "2026-06-20"
     release_note: "Live access check failed 2026-06-20; catalog remains partial pending a working source or confirmed mirror."
   - name: UT Ego
     kind: dataset
+    released: "2012-06"
     venue: "project page"
     year: 2012
     status: partial
     release_note: "Live source checked 2026-06-20; catalog remains partial because access, annotations, or terms are incomplete, gated, or not fully verified."
   - name: HUJI EgoSeg
     kind: dataset
+    released: "2014-06"
     venue: "project page"
     year: 2014
     status: partial
     release_note: "Live source checked 2026-06-20; catalog remains partial because access, annotations, or terms are incomplete, gated, or not fully verified."
   - name: FT-HID
     kind: dataset
+    released: "2022-09"
     venue: "GitHub"
     year: 2022
     status: open
     url: https://github.com/ENDLICHERE/FT-HID
+    paper: https://arxiv.org/abs/2209.10155
     scale: "90K+ RGB-D first- and third-person human interaction samples from 109 subjects"
     tasks: [action-recognition, procedure]
     license: "not specified"
   - name: Ego4D Benchmarks
     kind: benchmark
+    released: "2021-10"
     venue: "project page"
     year: 2021
     status: benchmark
     verified_at: "2026-06-20"
   - name: Ego-Exo4D Benchmarks
     kind: benchmark
+    released: "2023-11"
     venue: "project page"
     year: 2023
     status: benchmark
     verified_at: "2026-06-20"
   - name: EPIC-KITCHENS Challenges
     kind: benchmark
+    released: "2018-04"
     venue: "project page"
     year: 2018
     status: benchmark
     verified_at: "2026-06-20"
   - name: EgoMAS
     kind: model
+    released: "2026-03"
+    venue: "arXiv"
+    year: 2026
     status: open
     url: https://ma-egoqa.github.io/
+    paper: https://arxiv.org/abs/2603.09827
     scale: "Shared-memory baseline for multi-agent egocentric video QA"
     tasks: [video-language, representation-learning]
     verified_at: "2026-06-20"
     verified_at: "2026-06-20"
   - name: EgoPoseFormer
     kind: model
+    released: "2024-03"
+    venue: "arXiv"
+    year: 2024
     status: open
     url: https://github.com/ChenhongyiYang/egoposeformer
+    paper: https://arxiv.org/abs/2403.18080
     scale: "Transformer baseline for stereo egocentric 3D human pose estimation"
     tasks: [action, tracking, pose, hoi]
     verified_at: "2026-06-20"
     verified_at: "2026-06-20"
   - name: EgoHOS model
     kind: model
+    released: "2022-08"
     venue: "GitHub"
     year: 2022
     status: open
     url: https://github.com/owenzlz/EgoHOS
+    paper: https://arxiv.org/abs/2208.03826
     scale: "Context-aware hand-object segmentation and augmentation pipeline"
     tasks: [action, tracking, pose, hoi]
     verified_at: "2026-06-20"
   - name: AV-CONV
     kind: model
+    released: "2023-12"
+    venue: "arXiv"
     year: 2023
     status: open
     url: https://vjwq.github.io/AV-CONV/
+    paper: https://arxiv.org/abs/2312.12870
     scale: "Audio-visual conversational graph prediction from ego/exo conversation"
     tasks: [action, tracking, pose, hoi]
     verified_at: "2026-06-20"
     release_note: "Live source checked 2026-06-20; kept on watch until public artifacts, access terms, or reproducible release metadata are confirmed."
   - name: Ego4D CLI and docs
     kind: toolkit
+    released: "2021-10"
     venue: "project page"
     year: 2021
     status: open
     verified_at: "2026-06-20"
   - name: Ego-Exo4D CLI and docs
     kind: toolkit
+    released: "2023-11"
     venue: "project page"
     year: 2023
     status: open
     verified_at: "2026-06-20"
   - name: VISOR API
     kind: toolkit
+    released: "2022-09"
     venue: "GitHub"
     year: 2022
     status: open
     verified_at: "2026-06-20"
   - name: EgoObjects API
     kind: toolkit
+    released: "2023-09"
+    venue: "arXiv"
     year: 2023
     status: open
     url: https://github.com/facebookresearch/EgoObjects
+    paper: https://arxiv.org/abs/2309.08816
     scale: "Working with category and instance-level egocentric object labels."
     tasks: [tooling]
     verified_at: "2026-06-20"
   - name: HOT3D tooling
     kind: toolkit
+    released: "2024-06"
     venue: "project page"
     status: open
     url: https://facebookresearch.github.io/hot3d/
     verified_at: "2026-06-20"
   - name: HOI4D tooling
     kind: toolkit
+    released: "2022-03"
     venue: "project page"
     year: 2022
     status: open
     release_note: "Live source checked 2026-06-20; kept on watch until public artifacts, access terms, or reproducible release metadata are confirmed."
   - name: EgoMemory
     kind: benchmark
+    released: "2025-09"
     venue: "OpenReview"
+    year: 2025
     status: watch
     url: https://openreview.net/forum?id=T0em4hJCQb
     scale: "165,795 user-specific object annotations over 245 videos from 45 participants for memory-augmented personalized retrieval"
     release_note: "Live source checked 2026-06-20; kept on watch until public artifacts, access terms, or reproducible release metadata are confirmed."
   - name: EgoTextVQA
     kind: benchmark
+    released: "2025-06"
     venue: "CVPR 2025"
     year: 2025
     status: open
     release_note: "Live source checked 2026-06-20; kept on watch until public artifacts, access terms, or reproducible release metadata are confirmed."
   - name: EgoVQA
     kind: benchmark
+    released: "2019-10"
     venue: "ICCV 2019"
     year: 2019
     status: open
   - name: EgoSurgery
     kind: dataset
+    year: 2025
+    released: "2025-03"
+    venue: "arXiv"
     status: open
     url: https://github.com/Fujiry0/EgoSurgery
     paper: https://arxiv.org/abs/2503.18755
   - name: CMU-MMAC
     license: "not specified"
     license_url: "http://kitchen.cs.cmu.edu/"
+    milestone: "2009-06"
+    milestone_note: "The earliest egocentric dataset; launched egocentric activity recognition at the first IEEE Workshop on Egocentric Vision (CVPR 2009)."
     milestone_image: assets/milestones/cmu-mmac.png
     kind: dataset
+    released: "2009-06"
     venue: "CMU tech report 2009"
     year: 2009
     status: open
   - name: First-Person Social Interactions
     kind: dataset
+    released: "2012-06"
     venue: "CVPR 2012"
     year: 2012
     status: open
   - name: BEOID
     kind: dataset
+    released: "2014-09"
     venue: "BMVC 2014"
     year: 2014
     status: open
     modalities: [egocentric-video, latent-actions]
     tasks: [world-modeling, robot-learning, video-generation]
     verified_at: "2026-06-20"
+    milestone: "2026-02"
+    milestone_note: "44K-hour egocentric-video robot world model with latent actions for real-time planning."
+    milestone_image: assets/milestones/dreamdojo.png
     citation_key: dreamdojo_2026
   - name: UniDex
   - name: First-Person Pose Recognition
     kind: model
+    released: "2015-06"
     venue: "CVPR 2015"
     year: 2015
     status: watch

docs/maintenance.md CHANGED Viewed

@@ -52,6 +52,7 @@ machine-checked, so most mistakes are caught by the validator before review.
 ```bash
 ruby scripts/validate_catalog.rb
 ruby scripts/build_artifacts.rb --check
 ruby scripts/build_hf_package.rb --check
 ruby scripts/audit_catalog.rb
 ```

 ```bash
 ruby scripts/validate_catalog.rb
 ruby scripts/build_artifacts.rb --check
+ruby scripts/build_readme_i18n.rb --check
 ruby scripts/build_hf_package.rb --check
 ruby scripts/audit_catalog.rb
 ```

index.html CHANGED Viewed

@@ -5,16 +5,16 @@
     <meta name="viewport" content="width=device-width, initial-scale=1">
     <title>Awesome Egocentric Atlas</title>
     <!-- SEO:START (generated by build_artifacts.rb — do not edit by hand) -->
-    <meta name="description" content="Just launched Awesome Egocentric Atlas — a curated collection of first-person AI resources for egocentric vision, embodied AI, robotics, VLA, world models, WMA, memory, AR/VR, and hand-object interaction.">
     <link rel="canonical" href="https://chaoyue0307.github.io/awesome-egocentric-atlas/">
     <meta property="og:type" content="website">
     <meta property="og:url" content="https://chaoyue0307.github.io/awesome-egocentric-atlas/">
     <meta property="og:title" content="Awesome Egocentric Atlas">
-    <meta property="og:description" content="Just launched Awesome Egocentric Atlas — a curated collection of first-person AI resources for egocentric vision, embodied AI, robotics, VLA, world models, WMA, memory, AR/VR, and hand-object interaction.">
     <meta property="og:image" content="https://chaoyue0307.github.io/awesome-egocentric-atlas/assets/awesome-egocentric-atlas-cover.png">
     <meta name="twitter:card" content="summary_large_image">
     <meta name="twitter:title" content="Awesome Egocentric Atlas">
-    <meta name="twitter:description" content="Just launched Awesome Egocentric Atlas — a curated collection of first-person AI resources for egocentric vision, embodied AI, robotics, VLA, world models, WMA, memory, AR/VR, and hand-object interaction.">
     <meta name="twitter:image" content="https://chaoyue0307.github.io/awesome-egocentric-atlas/assets/awesome-egocentric-atlas-cover.png">
     <meta name="theme-color" content="#067882">
     <script type="application/ld+json">
@@ -22,7 +22,7 @@
   "@context": "https://schema.org",
   "@type": "DataCatalog",
   "name": "Awesome Egocentric Atlas",
-  "description": "Just launched Awesome Egocentric Atlas — a curated collection of first-person AI resources for egocentric vision, embodied AI, robotics, VLA, world models, WMA, memory, AR/VR, and hand-object interaction.",
   "url": "https://chaoyue0307.github.io/awesome-egocentric-atlas/",
   "sameAs": [
     "https://github.com/ChaoYue0307/awesome-egocentric-atlas",
@@ -969,7 +969,7 @@
       <section class="hero" id="top">
         <div class="hero-copy">
           <h1>Awesome Egocentric Atlas</h1>
-          <p class="lead" data-i18n="hero.lead">A curated map of first-person AI — the datasets, benchmarks, models, and tools behind egocentric vision, embodied AI and robotics, video-language, long-context memory, AR/VR, and hand-object interaction.</p>
           <div class="actions" aria-label="Project links">
             <a class="button primary" href="https://github.com/ChaoYue0307/awesome-egocentric-atlas" data-i18n="btn.github">GitHub Repo</a>
             <a class="button" href="https://huggingface.co/datasets/cy0307/awesome-egocentric-atlas" data-i18n="btn.hf">Hugging Face Mirror</a>
@@ -1004,16 +1004,16 @@
           </div>
         </div>
         <div class="hero-media" aria-label="Atlas visual preview">
-          <img src="assets/awesome-egocentric-atlas-cover.png" alt="Technical illustration of a first-person egocentric AI atlas">
           <div class="media-caption">
-            <span data-i18n="media.caption">First-person AI, mapped</span>
             <span data-updated>Updated 2026-06-20</span>
           </div>
         </div>
       </section>
       <section class="visual-band" aria-label="Atlas overview">
-        <img src="assets/awesome-egocentric-atlas-map.png" alt="Awesome Egocentric Atlas system map">
       </section>
       <section class="section milestones" id="milestones">
@@ -1024,9 +1024,11 @@
           </div>
         </div>
         <figure class="milestone-poster">
-          <img src="assets/awesome-egocentric-milestones.png" alt="Illustrated milestone timeline for representative egocentric AI works">
         </figure>
-        <ol class="milestone-grid" id="milestone-grid"></ol>
       </section>
       <section class="section" id="catalog">
@@ -1103,7 +1105,7 @@
         <div class="section-heading">
           <div>
             <h2 data-i18n="lanes.title">Research Lanes</h2>
-            <p data-i18n="lanes.desc">Six entry points for the main ways people use first-person data.</p>
           </div>
           <a class="text-link" href="https://github.com/ChaoYue0307/awesome-egocentric-atlas/blob/main/docs/taxonomy.md" data-i18n="lanes.taxonomy">Taxonomy</a>
         </div>
@@ -1117,8 +1119,8 @@
           <div class="status-list" id="status-list"></div>
         </div>
         <div class="figure-stack">
-          <img src="assets/awesome-egocentric-access-funnel.png" alt="Egocentric resources by access state">
-          <img src="assets/awesome-egocentric-timeline.png" alt="Egocentric resources by era and type">
         </div>
       </section>

     <meta name="viewport" content="width=device-width, initial-scale=1">
     <title>Awesome Egocentric Atlas</title>
     <!-- SEO:START (generated by build_artifacts.rb — do not edit by hand) -->
+    <meta name="description" content="Just launched Awesome Egocentric Atlas — a curated collection of egocentric AI resources for egocentric vision, embodied AI, robotics, VLA, world models, WMA, memory, AR/VR, and hand-object interaction.">
     <link rel="canonical" href="https://chaoyue0307.github.io/awesome-egocentric-atlas/">
     <meta property="og:type" content="website">
     <meta property="og:url" content="https://chaoyue0307.github.io/awesome-egocentric-atlas/">
     <meta property="og:title" content="Awesome Egocentric Atlas">
+    <meta property="og:description" content="Just launched Awesome Egocentric Atlas — a curated collection of egocentric AI resources for egocentric vision, embodied AI, robotics, VLA, world models, WMA, memory, AR/VR, and hand-object interaction.">
     <meta property="og:image" content="https://chaoyue0307.github.io/awesome-egocentric-atlas/assets/awesome-egocentric-atlas-cover.png">
     <meta name="twitter:card" content="summary_large_image">
     <meta name="twitter:title" content="Awesome Egocentric Atlas">
+    <meta name="twitter:description" content="Just launched Awesome Egocentric Atlas — a curated collection of egocentric AI resources for egocentric vision, embodied AI, robotics, VLA, world models, WMA, memory, AR/VR, and hand-object interaction.">
     <meta name="twitter:image" content="https://chaoyue0307.github.io/awesome-egocentric-atlas/assets/awesome-egocentric-atlas-cover.png">
     <meta name="theme-color" content="#067882">
     <script type="application/ld+json">
   "@context": "https://schema.org",
   "@type": "DataCatalog",
   "name": "Awesome Egocentric Atlas",
+  "description": "Just launched Awesome Egocentric Atlas — a curated collection of egocentric AI resources for egocentric vision, embodied AI, robotics, VLA, world models, WMA, memory, AR/VR, and hand-object interaction.",
   "url": "https://chaoyue0307.github.io/awesome-egocentric-atlas/",
   "sameAs": [
     "https://github.com/ChaoYue0307/awesome-egocentric-atlas",
       <section class="hero" id="top">
         <div class="hero-copy">
           <h1>Awesome Egocentric Atlas</h1>
+          <p class="lead" data-i18n="hero.lead">A curated map of egocentric AI — the datasets, benchmarks, models, and tools behind egocentric vision, embodied AI and robotics, video-language, long-context memory, AR/VR, and hand-object interaction.</p>
           <div class="actions" aria-label="Project links">
             <a class="button primary" href="https://github.com/ChaoYue0307/awesome-egocentric-atlas" data-i18n="btn.github">GitHub Repo</a>
             <a class="button" href="https://huggingface.co/datasets/cy0307/awesome-egocentric-atlas" data-i18n="btn.hf">Hugging Face Mirror</a>
           </div>
         </div>
         <div class="hero-media" aria-label="Atlas visual preview">
+          <img src="assets/awesome-egocentric-atlas-cover.png" width="1672" height="941" decoding="async" fetchpriority="high" alt="Technical illustration of a first-person egocentric AI atlas">
           <div class="media-caption">
+            <span data-i18n="media.caption">Egocentric AI, mapped</span>
             <span data-updated>Updated 2026-06-20</span>
           </div>
         </div>
       </section>
       <section class="visual-band" aria-label="Atlas overview">
+        <img src="assets/awesome-egocentric-atlas-map.png" width="3840" height="2190" loading="lazy" decoding="async" alt="Awesome Egocentric Atlas system map">
       </section>
       <section class="section milestones" id="milestones">
           </div>
         </div>
         <figure class="milestone-poster">
+          <div class="milestone-poster-frame">
+            <img src="assets/awesome-egocentric-milestones.png" width="3840" height="6966" loading="lazy" decoding="async" alt="Illustrated milestone timeline for representative egocentric AI works">
+            <div class="milestone-link-layer" id="milestone-link-layer" aria-label="Milestone work links"></div>
+          </div>
         </figure>
       </section>
       <section class="section" id="catalog">
         <div class="section-heading">
           <div>
             <h2 data-i18n="lanes.title">Research Lanes</h2>
+            <p data-i18n="lanes.desc">Six entry points for the main ways people use egocentric data.</p>
           </div>
           <a class="text-link" href="https://github.com/ChaoYue0307/awesome-egocentric-atlas/blob/main/docs/taxonomy.md" data-i18n="lanes.taxonomy">Taxonomy</a>
         </div>
           <div class="status-list" id="status-list"></div>
         </div>
         <div class="figure-stack">
+          <img src="assets/awesome-egocentric-access-funnel.png" width="3840" height="1560" loading="lazy" decoding="async" alt="Egocentric resources by access state">
+          <img src="assets/awesome-egocentric-timeline.png" width="3840" height="1680" loading="lazy" decoding="async" alt="Egocentric resources by era and type">
         </div>
       </section>

scripts/audit_catalog.rb CHANGED Viewed

@@ -55,7 +55,7 @@ puts
 puts "Generated: #{today}"
 puts "Total resources: #{summary.fetch('total_resources')} (#{summary.fetch('egocentric_resources')} egocentric, #{summary.fetch('adjacent_resources')} adjacent)"
 puts
-puts "## Counts"
 puts "Kinds:    #{count_by(egocentric, 'kind').map { |k, v| "#{k}=#{v}" }.join(', ')}"
 puts "Statuses: #{count_by(egocentric, 'status').map { |k, v| "#{k}=#{v}" }.join(', ')}"
 puts "Years:    #{count_by_value(egocentric.map { |entry| CatalogArtifacts.resource_year(entry) }).map { |k, v| "#{k}=#{v}" }.join(', ')}"

 puts "Generated: #{today}"
 puts "Total resources: #{summary.fetch('total_resources')} (#{summary.fetch('egocentric_resources')} egocentric, #{summary.fetch('adjacent_resources')} adjacent)"
 puts
+puts "## Egocentric Counts"
 puts "Kinds:    #{count_by(egocentric, 'kind').map { |k, v| "#{k}=#{v}" }.join(', ')}"
 puts "Statuses: #{count_by(egocentric, 'status').map { |k, v| "#{k}=#{v}" }.join(', ')}"
 puts "Years:    #{count_by_value(egocentric.map { |entry| CatalogArtifacts.resource_year(entry) }).map { |k, v| "#{k}=#{v}" }.join(', ')}"

scripts/build_readme_i18n.rb CHANGED Viewed

@@ -6,7 +6,9 @@
 # the translated pages are localized intros that link to the full catalog and
 # the interactive (multilingual) site.
 #
-# Usage: ruby scripts/build_readme_i18n.rb
 require "yaml"
@@ -22,49 +24,49 @@ LANG_NAME = {
 }.freeze
 T = {
-  "en" => { tagline: "A curated map of first-person AI — the datasets, benchmarks, models, and tools behind egocentric vision, embodied AI and robotics, video-language, long-context memory, AR/VR, and hand-object interaction.",
     resources: "egocentric resources", datasets: "datasets", benchmarks: "benchmarks", models: "models", toolkits: "toolkits",
     whats_inside: "What's inside", explore: "Explore", help: "Help translate", landing: "Landing page", hfmirror: "Hugging Face mirror",
     full: "Full catalog (English tables)", site: "Interactive site", hf: "Hugging Face dataset",
     note: "The detailed resource tables are maintained in English in the [main catalog](README.md) and the interactive site.",
     lanes: ["Foundation Video", "Procedure & Action", "Hands, Objects & 3D", "Memory & Reasoning", "Robotics & VLA", "AR & Wearables"] },
-  "zh" => { tagline: "第一人称 AI 的精选地图——汇集自我中心视觉、具身智能与机器人、视频语言、长上下文记忆、AR/VR 以及手物交互背后的数据集、基准、模型与工具。",
     resources: "自我中心资源", datasets: "数据集", benchmarks: "基准", models: "模型", toolkits: "工具包",
     whats_inside: "内容概览", explore: "探索", help: "帮助翻译", landing: "项目主页", hfmirror: "Hugging Face 镜像",
     full: "完整目录（英文表格）", site: "交互式网站", hf: "Hugging Face 数据集",
     note: "详细的资源表格以英文维护，见[主目录](README.md)和交互式网站。",
     lanes: ["基础视频", "流程与动作", "手、物体与 3D", "记忆与推理", "机器人与 VLA", "AR 与可穿戴"] },
-  "es" => { tagline: "Un mapa curado de la IA en primera persona: los conjuntos de datos, benchmarks, modelos y herramientas tras la visión egocéntrica, la IA encarnada y la robótica, el aprendizaje visión-lenguaje, la memoria de largo contexto, la RA/RV y la interacción mano-objeto.",
     resources: "recursos egocéntricos", datasets: "conjuntos de datos", benchmarks: "benchmarks", models: "modelos", toolkits: "herramientas",
     whats_inside: "Qué incluye", explore: "Explorar", help: "Ayuda a traducir", landing: "Página principal", hfmirror: "Espejo en Hugging Face",
     full: "Catálogo completo (tablas en inglés)", site: "Sitio interactivo", hf: "Conjunto de datos en Hugging Face",
     note: "Las tablas detalladas de recursos se mantienen en inglés en el [catálogo principal](README.md) y en el sitio interactivo.",
     lanes: ["Vídeo fundacional", "Procedimiento y acción", "Manos, objetos y 3D", "Memoria y razonamiento", "Robótica y VLA", "RA y wearables"] },
-  "fr" => { tagline: "Une carte sélective de l'IA à la première personne : les jeux de données, benchmarks, modèles et outils derrière la vision égocentrique, l'IA incarnée et la robotique, l'apprentissage vision-langage, la mémoire à long contexte, la RA/RV et l'interaction main-objet.",
     resources: "ressources égocentriques", datasets: "jeux de données", benchmarks: "benchmarks", models: "modèles", toolkits: "outils",
     whats_inside: "Contenu", explore: "Explorer", help: "Aider à traduire", landing: "Page d'accueil", hfmirror: "Miroir Hugging Face",
     full: "Catalogue complet (tableaux en anglais)", site: "Site interactif", hf: "Jeu de données Hugging Face",
     note: "Les tableaux détaillés des ressources sont maintenus en anglais dans le [catalogue principal](README.md) et sur le site interactif.",
     lanes: ["Vidéo fondationnelle", "Procédure et action", "Mains, objets et 3D", "Mémoire et raisonnement", "Robotique et VLA", "RA et wearables"] },
-  "de" => { tagline: "Eine kuratierte Karte der First-Person-KI – die Datensätze, Benchmarks, Modelle und Werkzeuge hinter egozentrischem Sehen, verkörperter KI und Robotik, Video-Sprache, Langzeitgedächtnis, AR/VR und Hand-Objekt-Interaktion.",
     resources: "egozentrische Ressourcen", datasets: "Datensätze", benchmarks: "Benchmarks", models: "Modelle", toolkits: "Toolkits",
     whats_inside: "Inhalt", explore: "Erkunden", help: "Beim Übersetzen helfen", landing: "Startseite", hfmirror: "Hugging-Face-Spiegel",
     full: "Vollständiger Katalog (englische Tabellen)", site: "Interaktive Website", hf: "Hugging-Face-Datensatz",
     note: "Die detaillierten Ressourcentabellen werden auf Englisch im [Hauptkatalog](README.md) und auf der interaktiven Website gepflegt.",
     lanes: ["Grundlagen-Video", "Ablauf und Aktion", "Hände, Objekte und 3D", "Gedächtnis und Schlussfolgern", "Robotik und VLA", "AR und Wearables"] },
-  "ja" => { tagline: "一人称視点 AI の厳選マップ——エゴセントリック視覚、身体性 AI とロボティクス、ビデオ言語、長文脈記憶、AR/VR、手と物体の相互作用を支えるデータセット・ベンチマーク・モデル・ツールを収録。",
     resources: "エゴセントリック資源", datasets: "データセット", benchmarks: "ベンチマーク", models: "モデル", toolkits: "ツールキット",
     whats_inside: "収録内容", explore: "探索", help: "翻訳に協力", landing: "ランディングページ", hfmirror: "Hugging Face ミラー",
     full: "完全なカタログ（英語の表）", site: "インタラクティブサイト", hf: "Hugging Face データセット",
     note: "詳細な資源の表は英語で[メインカタログ](README.md)とインタラクティブサイトに維持されています。",
     lanes: ["基盤映像", "手順と行動", "手・物体・3D", "記憶と推論", "ロボティクスと VLA", "AR とウェアラブル"] },
-  "ko" => { tagline: "1인칭 AI의 엄선된 지도 — 자기중심 비전, 체화 AI와 로보틱스, 비디오-언어, 장문맥 기억, AR/VR, 손-물체 상호작용을 뒷받침하는 데이터셋·벤치마크·모델·도구를 담았습니다.",
     resources: "자기중심 자원", datasets: "데이터셋", benchmarks: "벤치마크", models: "모델", toolkits: "툴킷",
     whats_inside: "구성", explore: "둘러보기", help: "번역 돕기", landing: "랜딩 페이지", hfmirror: "Hugging Face 미러",
     full: "전체 카탈로그 (영문 표)", site: "인터랙티브 사이트", hf: "Hugging Face 데이터셋",
     note: "상세 자원 표는 [메인 카탈로그](README.md)와 인터랙티브 사이트에 영어로 유지됩니다.",
     lanes: ["기반 비디오", "절차와 행동", "손·물체·3D", "기억과 추론", "로보틱스와 VLA", "AR과 웨어러블"] },
-  "pt" => { tagline: "Um mapa curado da IA em primeira pessoa: os conjuntos de dados, benchmarks, modelos e ferramentas por trás da visão egocêntrica, da IA incorporada e da robótica, da aprendizagem visão-linguagem, da memória de longo contexto, da RA/RV e da interação mão-objeto.",
     resources: "recursos egocêntricos", datasets: "conjuntos de dados", benchmarks: "benchmarks", models: "modelos", toolkits: "ferramentas",
     whats_inside: "O que inclui", explore: "Explorar", help: "Ajude a traduzir", landing: "Página inicial", hfmirror: "Espelho Hugging Face",
     full: "Catálogo completo (tabelas em inglês)", site: "Site interativo", hf: "Conjunto de dados Hugging Face",
@@ -95,6 +97,17 @@ ego = resources.reject { |r| r["scope"] == "adjacent" }
 kc = Hash.new(0)
 ego.each { |r| kc[r["kind"]] += 1 }
 COUNTS = { ego: ego.length, dataset: kc["dataset"], benchmark: kc["benchmark"], model: kc["model"], toolkit: kc["toolkit"] }
 # --- update README.md language bar (between markers) -------------------------
 START = "<!-- LANG-BAR:START -->"
@@ -108,7 +121,7 @@ else
   # insert right after the tagline paragraph (first centered <strong>…</strong></p>)
   readme.sub!(/(<strong>[^<]*<\/strong>\s*<\/p>\n)/m, "\\1\n#{block}\n")
 end
-File.write(readme_path, readme)
 # --- generate translated landing pages --------------------------------------
 LANGS.each do |lang|
@@ -144,7 +157,18 @@ LANGS.each do |lang|
     > #{t[:note]}
   MD
-  File.write(File.join(ROOT, readme_for(lang)), body)
 end
-puts "Wrote README language bar + #{LANGS.length - 1} translated landing pages (#{COUNTS[:ego]} egocentric)."

 # the translated pages are localized intros that link to the full catalog and
 # the interactive (multilingual) site.
 #
+# Usage:
+#   ruby scripts/build_readme_i18n.rb
+#   ruby scripts/build_readme_i18n.rb --check
 require "yaml"
 }.freeze
 T = {
+  "en" => { tagline: "A curated map of egocentric AI — the datasets, benchmarks, models, and tools behind egocentric vision, embodied AI and robotics, video-language, long-context memory, AR/VR, and hand-object interaction.",
     resources: "egocentric resources", datasets: "datasets", benchmarks: "benchmarks", models: "models", toolkits: "toolkits",
     whats_inside: "What's inside", explore: "Explore", help: "Help translate", landing: "Landing page", hfmirror: "Hugging Face mirror",
     full: "Full catalog (English tables)", site: "Interactive site", hf: "Hugging Face dataset",
     note: "The detailed resource tables are maintained in English in the [main catalog](README.md) and the interactive site.",
     lanes: ["Foundation Video", "Procedure & Action", "Hands, Objects & 3D", "Memory & Reasoning", "Robotics & VLA", "AR & Wearables"] },
+  "zh" => { tagline: "自我中心 AI 的精选地图——汇集自我中心视觉、具身智能与机器人、视频语言、长上下文记忆、AR/VR 以及手物交互背后的数据集、基准、模型与工具。",
     resources: "自我中心资源", datasets: "数据集", benchmarks: "基准", models: "模型", toolkits: "工具包",
     whats_inside: "内容概览", explore: "探索", help: "帮助翻译", landing: "项目主页", hfmirror: "Hugging Face 镜像",
     full: "完整目录（英文表格）", site: "交互式网站", hf: "Hugging Face 数据集",
     note: "详细的资源表格以英文维护，见[主目录](README.md)和交互式网站。",
     lanes: ["基础视频", "流程与动作", "手、物体与 3D", "记忆与推理", "机器人与 VLA", "AR 与可穿戴"] },
+  "es" => { tagline: "Un mapa curado de la IA egocéntrica: los conjuntos de datos, benchmarks, modelos y herramientas tras la visión egocéntrica, la IA encarnada y la robótica, el aprendizaje visión-lenguaje, la memoria de largo contexto, la RA/RV y la interacción mano-objeto.",
     resources: "recursos egocéntricos", datasets: "conjuntos de datos", benchmarks: "benchmarks", models: "modelos", toolkits: "herramientas",
     whats_inside: "Qué incluye", explore: "Explorar", help: "Ayuda a traducir", landing: "Página principal", hfmirror: "Espejo en Hugging Face",
     full: "Catálogo completo (tablas en inglés)", site: "Sitio interactivo", hf: "Conjunto de datos en Hugging Face",
     note: "Las tablas detalladas de recursos se mantienen en inglés en el [catálogo principal](README.md) y en el sitio interactivo.",
     lanes: ["Vídeo fundacional", "Procedimiento y acción", "Manos, objetos y 3D", "Memoria y razonamiento", "Robótica y VLA", "RA y wearables"] },
+  "fr" => { tagline: "Une carte sélective de l'IA égocentrique : les jeux de données, benchmarks, modèles et outils derrière la vision égocentrique, l'IA incarnée et la robotique, l'apprentissage vision-langage, la mémoire à long contexte, la RA/RV et l'interaction main-objet.",
     resources: "ressources égocentriques", datasets: "jeux de données", benchmarks: "benchmarks", models: "modèles", toolkits: "outils",
     whats_inside: "Contenu", explore: "Explorer", help: "Aider à traduire", landing: "Page d'accueil", hfmirror: "Miroir Hugging Face",
     full: "Catalogue complet (tableaux en anglais)", site: "Site interactif", hf: "Jeu de données Hugging Face",
     note: "Les tableaux détaillés des ressources sont maintenus en anglais dans le [catalogue principal](README.md) et sur le site interactif.",
     lanes: ["Vidéo fondationnelle", "Procédure et action", "Mains, objets et 3D", "Mémoire et raisonnement", "Robotique et VLA", "RA et wearables"] },
+  "de" => { tagline: "Eine kuratierte Karte egozentrischer KI – die Datensätze, Benchmarks, Modelle und Werkzeuge hinter egozentrischem Sehen, verkörperter KI und Robotik, Video-Sprache, Langzeitgedächtnis, AR/VR und Hand-Objekt-Interaktion.",
     resources: "egozentrische Ressourcen", datasets: "Datensätze", benchmarks: "Benchmarks", models: "Modelle", toolkits: "Toolkits",
     whats_inside: "Inhalt", explore: "Erkunden", help: "Beim Übersetzen helfen", landing: "Startseite", hfmirror: "Hugging-Face-Spiegel",
     full: "Vollständiger Katalog (englische Tabellen)", site: "Interaktive Website", hf: "Hugging-Face-Datensatz",
     note: "Die detaillierten Ressourcentabellen werden auf Englisch im [Hauptkatalog](README.md) und auf der interaktiven Website gepflegt.",
     lanes: ["Grundlagen-Video", "Ablauf und Aktion", "Hände, Objekte und 3D", "Gedächtnis und Schlussfolgern", "Robotik und VLA", "AR und Wearables"] },
+  "ja" => { tagline: "エゴセントリック AI の厳選マップ——エゴセントリック視覚、身体性 AI とロボティクス、ビデオ言語、長文脈記憶、AR/VR、手と物体の相互作用を支えるデータセット・ベンチマーク・モデル・ツールを収録。",
     resources: "エゴセントリック資源", datasets: "データセット", benchmarks: "ベンチマーク", models: "モデル", toolkits: "ツールキット",
     whats_inside: "収録内容", explore: "探索", help: "翻訳に協力", landing: "ランディングページ", hfmirror: "Hugging Face ミラー",
     full: "完全なカタログ（英語の表）", site: "インタラクティブサイト", hf: "Hugging Face データセット",
     note: "詳細な資源の表は英語で[メインカタログ](README.md)とインタラクティブサイトに維持されています。",
     lanes: ["基盤映像", "手順と行動", "手・物体・3D", "記憶と推論", "ロボティクスと VLA", "AR とウェアラブル"] },
+  "ko" => { tagline: "자기중심 AI의 엄선된 지도 — 자기중심 비전, 체화 AI와 로보틱스, 비디오-언어, 장문맥 기억, AR/VR, 손-물체 상호작용을 뒷받침하는 데이터셋·벤치마크·모델·도구를 담았습니다.",
     resources: "자기중심 자원", datasets: "데이터셋", benchmarks: "벤치마크", models: "모델", toolkits: "툴킷",
     whats_inside: "구성", explore: "둘러보기", help: "번역 돕기", landing: "랜딩 페이지", hfmirror: "Hugging Face 미러",
     full: "전체 카탈로그 (영문 표)", site: "인터랙티브 사이트", hf: "Hugging Face 데이터셋",
     note: "상세 자원 표는 [메인 카탈로그](README.md)와 인터랙티브 사이트에 영어로 유지됩니다.",
     lanes: ["기반 비디오", "절차와 행동", "손·물체·3D", "기억과 추론", "로보틱스와 VLA", "AR과 웨어러블"] },
+  "pt" => { tagline: "Um mapa curado da IA egocêntrica: os conjuntos de dados, benchmarks, modelos e ferramentas por trás da visão egocêntrica, da IA incorporada e da robótica, da aprendizagem visão-linguagem, da memória de longo contexto, da RA/RV e da interação mão-objeto.",
     resources: "recursos egocêntricos", datasets: "conjuntos de dados", benchmarks: "benchmarks", models: "modelos", toolkits: "ferramentas",
     whats_inside: "O que inclui", explore: "Explorar", help: "Ajude a traduzir", landing: "Página inicial", hfmirror: "Espelho Hugging Face",
     full: "Catálogo completo (tabelas em inglês)", site: "Site interativo", hf: "Conjunto de dados Hugging Face",
 kc = Hash.new(0)
 ego.each { |r| kc[r["kind"]] += 1 }
 COUNTS = { ego: ego.length, dataset: kc["dataset"], benchmark: kc["benchmark"], model: kc["model"], toolkit: kc["toolkit"] }
+CHECK = ARGV.include?("--check")
+stale = []
+def write_or_check(path, expected, check, stale)
+  if check
+    rel = path.sub("#{ROOT}/", "")
+    stale << rel unless File.file?(path) && File.read(path, encoding: "UTF-8") == expected
+  else
+    File.write(path, expected, encoding: "UTF-8")
+  end
+end
 # --- update README.md language bar (between markers) -------------------------
 START = "<!-- LANG-BAR:START -->"
   # insert right after the tagline paragraph (first centered <strong>…</strong></p>)
   readme.sub!(/(<strong>[^<]*<\/strong>\s*<\/p>\n)/m, "\\1\n#{block}\n")
 end
+write_or_check(readme_path, readme, CHECK, stale)
 # --- generate translated landing pages --------------------------------------
 LANGS.each do |lang|
     > #{t[:note]}
   MD
+  write_or_check(File.join(ROOT, readme_for(lang)), body, CHECK, stale)
 end
+if CHECK
+  unless stale.empty?
+    warn "README i18n artifact(s) out of date:"
+    stale.each { |path| warn "  - #{path}" }
+    warn "Run: ruby scripts/build_readme_i18n.rb"
+    exit 1
+  end
+  puts "README i18n OK: #{LANGS.length - 1} translated landing pages (#{COUNTS[:ego]} egocentric)."
+else
+  puts "Wrote README language bar + #{LANGS.length - 1} translated landing pages (#{COUNTS[:ego]} egocentric)."
+end

scripts/lib/catalog_artifacts.rb CHANGED Viewed

@@ -191,6 +191,7 @@ module CatalogArtifacts
       "summary" => summary(normalized),
       "lanes" => lane_payload(normalized),
       "milestones" => milestones,
       "resources" => normalized
     }
   end
@@ -266,7 +267,7 @@ module CatalogArtifacts
     replace_once!(
       text,
       /(<!-- MILESTONES:START[^>]*-->).*?(<!-- MILESTONES:END -->)/m,
-      ->(m) { "#{m[1]}\n\n#{milestones_markdown}\n\n#{m[2]}" },
       "README milestones section"
     )
     text
@@ -485,7 +486,7 @@ module CatalogArtifacts
     trend_points = points.map { |x, y| "#{x},#{fmt.call(y)}" }.join(" ")
     last_x, last_y = points.last
     projection = %(M#{last_x} #{fmt.call(last_y)} L1230 #{fmt.call(last_y - 17)})
-    note_y = [145, last_y - 25].max
     circles = points.each_with_index.map do |(x, y), idx|
       fill = idx == points.length - 1 ? "#ef9f24" : "#ffffff"
@@ -570,7 +571,7 @@ module CatalogArtifacts
         <polyline class="trend" points="#{trend_points}"/>
         <path class="proj" d="#{projection}" marker-end="url(#up)"/>
-        <text class="note" x="1208" y="#{fmt.call(note_y)}" text-anchor="end">still H1</text>
         <g>
           #{circles}
         </g>
@@ -582,22 +583,95 @@ module CatalogArtifacts
     SVG
   end
-  def updated_access_funnel_svg(content = File.read(File.join(ROOT, "assets", "awesome-egocentric-access-funnel.svg"), encoding: "UTF-8"))
     summary_data = summary
     total = summary_data.fetch("egocentric_resources")
     statuses = summary_data.fetch("status_counts")
-    text = content.dup
-    replace_once!(text, /A funnel-style snapshot of the \d+ egocentric resources/, "A funnel-style snapshot of the #{total} egocentric resources", "access desc total")
-    replace_once!(text, />\d+ egocentric resources by access state</, ">#{total} egocentric resources by access state<", "access title total")
-    STATUS_ORDER.each do |status|
-      replace_once!(
-        text,
-        /(>#{Regexp.escape(status)} &#183; )\d+(<\/text>)/,
-        "\\1#{statuses.fetch(status, 0)}\\2",
-        "access #{status} count"
-      )
-    end
-    text
   end
   def updated_atlas_map_svg(content = File.read(File.join(ROOT, "assets", "awesome-egocentric-atlas-map.svg"), encoding: "UTF-8"))
@@ -667,59 +741,247 @@ module CatalogArtifacts
     end.join("\n")
   end
-  def updated_milestones_svg(_content = nil)
-    items = milestones
-    card_width = 370
-    card_height = 552
-    card_gap_x = 45
-    card_gap_y = 34
-    margin_x = 40
-    grid_top = 168
-    cols = 3
-    rows = (items.length.to_f / cols).ceil
-    height = grid_top + (rows * card_height) + ([rows - 1, 0].max * card_gap_y) + 80
-    bottom_y = height - 34
     years = items.map { |item| item.fetch("date").to_s[/\d{4}/].to_i }.reject(&:zero?)
     year_span = years.empty? ? "field milestones" : "#{years.min}-#{years.max}"
-    cards = items.each_with_index.map do |item, index|
-      col = index % cols
-      row = index / cols
-      x = margin_x + (col * (card_width + card_gap_x))
-      y = grid_top + (row * (card_height + card_gap_y))
-      image = item["image"].to_s.sub(%r{\Aassets/}, "")
-      date = item.fetch("date")
-      kind = item.fetch("kind")
-      date_width = [date.length * 8 + 26, 78].max
-      kind_width = [kind.length * 8 + 32, 80].max
-      name_lines = wrap_text(item.fetch("name"), max_chars: 24, max_lines: 2)
-      note_lines = wrap_text(item.fetch("note"), max_chars: 43, max_lines: 2)
-      <<~CARD
-        <g class="card" transform="translate(#{x} #{y})">
-          <rect class="card-bg" width="#{card_width}" height="#{card_height}" rx="18"/>
-          <rect class="image-frame" x="18" y="78" width="334" height="334" rx="14"/>
-          <image href="#{html_escape(image)}" x="18" y="78" width="334" height="334" preserveAspectRatio="xMidYMid meet"/>
-          <rect class="date-pill" x="18" y="22" width="#{date_width}" height="32" rx="16"/>
-          <text class="pill-text" x="#{18 + (date_width / 2.0)}" y="43" text-anchor="middle">#{html_escape(date)}</text>
-          <rect class="kind-pill" x="#{26 + date_width}" y="22" width="#{kind_width}" height="32" rx="16"/>
-          <text class="pill-text" x="#{26 + date_width + (kind_width / 2.0)}" y="43" text-anchor="middle">#{html_escape(kind)}</text>
-          #{svg_text_block(name_lines, x: 18, y: 448, class_name: "card-title", line_height: 25)}
-          #{svg_text_block(note_lines, x: 18, y: 502, class_name: "card-note", line_height: 19)}
         </g>
-      CARD
     end.join("\n")
     <<~SVG
       <svg xmlns="http://www.w3.org/2000/svg" width="1280" height="#{height}" viewBox="0 0 1280 #{height}" role="img" aria-labelledby="title desc">
         <title id="title">Representative egocentric AI milestones</title>
-        <desc id="desc">A generated milestone poster for Awesome Egocentric Atlas, showing #{items.length} representative field-defining works from #{year_span}. Card labels come from data/resources.yml and image panels are shown uncropped.</desc>
         <defs>
           <linearGradient id="bg" x1="0" y1="0" x2="1" y2="1">
             <stop offset="0" stop-color="#fbfdff"/>
             <stop offset="0.56" stop-color="#f4faf9"/>
             <stop offset="1" stop-color="#eaf6f5"/>
           </linearGradient>
           <pattern id="dots" width="28" height="28" patternUnits="userSpaceOnUse">
             <circle cx="2" cy="2" r="1.35" fill="#cfe0e4" opacity="0.38"/>
           </pattern>
@@ -728,39 +990,49 @@ module CatalogArtifacts
           </filter>
           <style>
             .kicker { font: 800 15px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #0b8f98; letter-spacing: .18em; }
-            .title { font: 800 44px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #14212b; letter-spacing: 0; }
             .subtitle { font: 500 18px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #53606b; letter-spacing: 0; }
             .stat-num { font: 900 43px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #0f3b45; letter-spacing: 0; }
             .stat-range { font: 900 34px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #0f3b45; letter-spacing: 0; }
             .stat-label { font: 700 14px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #61717b; letter-spacing: .08em; }
-            .card-bg { fill: #ffffff; fill-opacity: .94; stroke: #c9dde2; stroke-width: 1.4; filter: url(#softShadow); }
-            .image-frame { fill: #eef6f5; stroke: #d7e5e8; stroke-width: 1.2; }
             .date-pill { fill: #0b8f98; }
             .kind-pill { fill: #ef9f24; }
-            .pill-text { font: 800 14px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #ffffff; letter-spacing: .02em; }
-            .card-title { font: 800 21px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #182733; letter-spacing: 0; }
-            .card-note { font: 500 14.5px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #5c6b74; letter-spacing: 0; }
             .rule { stroke: #c8dde2; stroke-width: 1.4; }
-            .foot { font: 600 15px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #62727c; letter-spacing: 0; }
           </style>
         </defs>
         <rect width="1280" height="#{height}" fill="url(#bg)"/>
         <rect width="1280" height="#{height}" fill="url(#dots)"/>
-        <line class="rule" x1="40" y1="128" x2="1240" y2="128"/>
-        <text class="kicker" x="40" y="54">CURATED FIELD MILESTONES</text>
-        <text class="title" x="40" y="100">Egocentric AI timeline</text>
-        <text class="subtitle" x="40" y="137">Representative works that changed first-person vision, AR/wearables, robotics, VLA, memory, and hand-object understanding.</text>
         <text class="stat-range" x="1014" y="82" text-anchor="middle">#{year_span}</text>
         <text class="stat-label" x="1014" y="106" text-anchor="middle">SPAN</text>
         <text class="stat-num" x="1172" y="82" text-anchor="middle">#{items.length}</text>
         <text class="stat-label" x="1172" y="106" text-anchor="middle">WORKS</text>
-      #{cards}
-        <text class="foot" x="640" y="#{bottom_y}" text-anchor="middle">Generated from data/resources.yml milestone fields; visual panels are local text-free ChatGPT image assets.</text>
       </svg>
     SVG
   end

       "summary" => summary(normalized),
       "lanes" => lane_payload(normalized),
       "milestones" => milestones,
+      "milestone_layout" => milestone_link_layout,
       "resources" => normalized
     }
   end
     replace_once!(
       text,
       /(<!-- MILESTONES:START[^>]*-->).*?(<!-- MILESTONES:END -->)/m,
+      ->(m) { "#{m[1]}\n\n#{m[2]}" },
       "README milestones section"
     )
     text
     trend_points = points.map { |x, y| "#{x},#{fmt.call(y)}" }.join(" ")
     last_x, last_y = points.last
     projection = %(M#{last_x} #{fmt.call(last_y)} L1230 #{fmt.call(last_y - 17)})
+    note_y = [135, last_y - 38].max
     circles = points.each_with_index.map do |(x, y), idx|
       fill = idx == points.length - 1 ? "#ef9f24" : "#ffffff"
         <polyline class="trend" points="#{trend_points}"/>
         <path class="proj" d="#{projection}" marker-end="url(#up)"/>
+        <text class="note" x="1210" y="#{fmt.call(note_y)}" text-anchor="middle">H1 only</text>
         <g>
           #{circles}
         </g>
     SVG
   end
+  def updated_access_funnel_svg(_content = nil)
     summary_data = summary
     total = summary_data.fetch("egocentric_resources")
     statuses = summary_data.fetch("status_counts")
+    max_count = STATUS_ORDER.map { |status| statuses.fetch(status, 0) }.max.to_f
+    max_count = 1.0 if max_count.zero?
+    rail_x = 300
+    rail_width = 560.0
+    row_y = 186
+    row_gap = 64
+    row_height = 40
+    colors = {
+      "open" => "#00a6b2",
+      "watch" => "#9aa7af",
+      "partial" => "#ef9f24",
+      "benchmark" => "#5b6b73",
+      "request" => "#d98f1f"
+    }
+    gloss = {
+      "open" => "usable today",
+      "watch" => "verify before use",
+      "partial" => "subset / annotations only",
+      "benchmark" => "eval over existing video",
+      "request" => "license / approval needed"
+    }
+    rows = STATUS_ORDER.each_with_index.map do |status, index|
+      count = statuses.fetch(status, 0)
+      percent = total.positive? ? ((count * 100.0) / total) : 0.0
+      fill_width = count.zero? ? 0.0 : [(count / max_count) * rail_width, 8.0].max
+      y = row_y + (index * row_gap)
+      label = "#{status} · #{count}"
+      pct_label = format("%.1f%%", percent)
+      <<~ROW
+        <g>
+          <text class="rowlabel" x="70" y="#{y + 27}">#{html_escape(label)}</text>
+          <rect class="rail" x="#{rail_x}" y="#{y}" width="#{timeline_number(rail_width)}" height="#{row_height}" rx="12"/>
+          <rect class="fill" x="#{rail_x}" y="#{y}" width="#{timeline_number(fill_width)}" height="#{row_height}" rx="12" fill="#{colors.fetch(status)}"/>
+          <text class="value" x="#{rail_x + rail_width + 28}" y="#{y + 27}">#{pct_label}</text>
+          <text class="gloss" x="#{rail_x + rail_width + 126}" y="#{y + 27}">#{html_escape(gloss.fetch(status))}</text>
+        </g>
+      ROW
+    end.join("\n")
+    <<~SVG
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1280 520" role="img" aria-labelledby="title desc">
+        <title id="title">Egocentric resources by access state</title>
+        <desc id="desc">A proportional bar snapshot of the #{total} egocentric resources by public-access state: open, watch, partial, benchmark, and request. Bar lengths are generated from the latest catalog counts.</desc>
+        <defs>
+          <linearGradient id="bg" x1="0" x2="1" y1="0" y2="1">
+            <stop offset="0" stop-color="#fbfdff"/>
+            <stop offset="1" stop-color="#eef7f6"/>
+          </linearGradient>
+          <pattern id="dots" width="32" height="32" patternUnits="userSpaceOnUse">
+            <circle cx="2" cy="2" r="1.6" fill="#cfe0e4" opacity="0.4"/>
+          </pattern>
+          <filter id="shadow" x="-6%" y="-18%" width="112%" height="145%">
+            <feDropShadow dx="0" dy="5" stdDeviation="6" flood-color="#0f2a33" flood-opacity="0.10"/>
+          </filter>
+          <style>
+            .ink { fill: #14212b; }
+            .kicker { font: 700 15px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #0b8f98; letter-spacing: .16em; }
+            .title { font: 700 37px system-ui, -apple-system, "Segoe UI", sans-serif; }
+            .subtitle { font: 500 17px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #53606b; }
+            .rowlabel { font: 760 18px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #24323c; }
+            .value { font: 760 18px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #24323c; }
+            .gloss { font: 520 16px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #6a7882; }
+            .rail { fill: #e8f1f3; stroke: #c9dde2; stroke-width: 1.1; }
+            .fill { filter: url(#shadow); }
+            .scale { font: 620 12px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #85939c; }
+            .tick { stroke: #c9dde2; stroke-width: 1; }
+          </style>
+        </defs>
+        <rect width="1280" height="520" fill="url(#bg)"/>
+        <rect width="1280" height="520" fill="url(#dots)"/>
+        <text class="kicker" x="70" y="58">ACCESS REALITY</text>
+        <text class="title ink" x="70" y="102">#{total} egocentric resources by access state</text>
+        <text class="subtitle" x="70" y="134">Bar lengths are proportional to live status counts. Labels stay outside the bars so small categories remain readable.</text>
+        <line class="tick" x1="#{rail_x}" y1="162" x2="#{rail_x}" y2="174"/>
+        <line class="tick" x1="#{rail_x + rail_width}" y1="162" x2="#{rail_x + rail_width}" y2="174"/>
+        <text class="scale" x="#{rail_x}" y="156" text-anchor="middle">0</text>
+        <text class="scale" x="#{rail_x + rail_width}" y="156" text-anchor="middle">max #{max_count.to_i}</text>
+        #{rows}
+      </svg>
+    SVG
   end
   def updated_atlas_map_svg(content = File.read(File.join(ROOT, "assets", "awesome-egocentric-atlas-map.svg"), encoding: "UTF-8"))
     end.join("\n")
   end
+  def milestone_poster_layout(items = milestones)
     years = items.map { |item| item.fetch("date").to_s[/\d{4}/].to_i }.reject(&:zero?)
     year_span = years.empty? ? "field milestones" : "#{years.min}-#{years.max}"
+    year_for = ->(item) { item.fetch("date").to_s[/\d{4}/].to_i }
+    era_specs = [
+      {
+        label: "Origins",
+        range: "2009-2015",
+        theme: "Egocentric activity, gaze, hands, and daily life become measurable.",
+        color: "#0b8f98",
+        accent: "#ef9f24",
+        items: items.select { |item| year_for.call(item) <= 2018 }
+      },
+      {
+        label: "Modern Scale",
+        range: "2020-2022",
+        theme: "Large benchmarks, smart-glasses sensing, geometry, and video-language pretraining.",
+        color: "#0f5d68",
+        accent: "#7a6ff0",
+        items: items.select { |item| (2019..2022).cover?(year_for.call(item)) }
+      },
+      {
+        label: "Reasoning & Robotics",
+        range: "2023-2024",
+        theme: "Long-form reasoning, ego-exo capture, AR hand-object tracking, and robot interfaces.",
+        color: "#34424d",
+        accent: "#0b8f98",
+        items: items.select { |item| (2023..2024).cover?(year_for.call(item)) }
+      },
+      {
+        label: "Daily Life to VLA",
+        range: "2025",
+        theme: "Personal memory and egocentric demonstrations begin feeding robot policies.",
+        color: "#98620b",
+        accent: "#0b8f98",
+        items: items.select { |item| year_for.call(item) == 2025 }
+      },
+      {
+        label: "World-Model Frontier",
+        range: "2026",
+        theme: "Egocentric corpora, world models, and data-scaling laws push embodied AI outward.",
+        color: "#5d52cf",
+        accent: "#ef9f24",
+        items: items.select { |item| year_for.call(item) >= 2026 }
+      }
+    ].reject { |group| group.fetch(:items).empty? }
+    margin_x = 40
+    row_gap = 26
+    header_height = 188
+    row_width = 1200
+    card_area_x = 266
+    card_area_width = 954
+    card_gap = 14
+    era_dot_x = card_area_x - margin_x - 18
+    row_layouts = era_specs.map do |group|
+      count = group.fetch(:items).length
+      card_width = [
+        [
+          ((card_area_width - (card_gap * [count - 1, 0].max)) / count.to_f).floor,
+          174
+        ].max,
+        286
+      ].min
+      image_height = [
+        [
+          (card_width * 0.62).round,
+          104
+        ].max,
+        158
+      ].min
+      card_height = image_height + 154
+      row_height = card_height + 104
+      group.merge(card_width: card_width, image_height: image_height, card_height: card_height, row_height: row_height)
+    end
+    height = header_height + row_layouts.sum { |group| group.fetch(:row_height) } + (row_gap * [row_layouts.length - 1, 0].max) + 31
+    current_y = header_height
+    era_nodes = []
+    rows = row_layouts.each_with_index.map do |group, group_index|
+      y = current_y
+      current_y += group.fetch(:row_height) + row_gap
+      era_nodes << [margin_x + era_dot_x, y + 54]
+      count = group.fetch(:items).length
+      cards_width = (count * group.fetch(:card_width)) + (card_gap * [count - 1, 0].max)
+      start_x = card_area_x + ((card_area_width - cards_width) / 2.0)
+      card_y = y + 78
+      cells = group.fetch(:items).each_with_index.map do |item, index|
+        card_width = group.fetch(:card_width)
+        image_height = group.fetch(:image_height)
+        card_height = group.fetch(:card_height)
+        x = start_x + (index * (card_width + card_gap))
+        {
+          item: item,
+          x: x,
+          y: card_y,
+          width: card_width,
+          height: card_height,
+          image_height: image_height
+        }
+      end
+      group.merge(index: group_index, y: y, cells: cells)
+    end
+    {
+      items: items,
+      year_span: year_span,
+      width: 1280,
+      height: height,
+      margin_x: margin_x,
+      row_width: row_width,
+      card_area_x: card_area_x,
+      era_dot_x: era_dot_x,
+      era_nodes: era_nodes,
+      rows: rows
+    }
+  end
+  def milestone_link_layout
+    layout = milestone_poster_layout
+    {
+      "width" => layout.fetch(:width),
+      "height" => layout.fetch(:height),
+      "cells" => layout.fetch(:rows).flat_map do |group|
+        group.fetch(:cells).map do |cell|
+          item = cell.fetch(:item)
+          {
+            "name" => item.fetch("name"),
+            "date" => item.fetch("date"),
+            "kind" => item.fetch("kind"),
+            "url" => item.fetch("url"),
+            "x" => cell.fetch(:x).round(2),
+            "y" => cell.fetch(:y).round(2),
+            "width" => cell.fetch(:width),
+            "height" => cell.fetch(:height)
+          }
+        end
+      end
+    }
+  end
+  def updated_milestones_svg(_content = nil)
+    layout = milestone_poster_layout
+    items = layout.fetch(:items)
+    year_span = layout.fetch(:year_span)
+    height = layout.fetch(:height)
+    margin_x = layout.fetch(:margin_x)
+    row_width = layout.fetch(:row_width)
+    card_area_x = layout.fetch(:card_area_x)
+    era_dot_x = layout.fetch(:era_dot_x)
+    era_nodes = layout.fetch(:era_nodes)
+    kind_colors = {
+      "dataset" => "#0b8f98",
+      "benchmark" => "#5b6b73",
+      "model" => "#ef9f24",
+      "toolkit" => "#7a6ff0",
+      "collection" => "#b8c2c9"
+    }
+    bands = layout.fetch(:rows).map do |group|
+      y = group.fetch(:y)
+      cards = group.fetch(:cells).map do |cell|
+        item = cell.fetch(:item)
+        card_width = cell.fetch(:width)
+        image_height = cell.fetch(:image_height)
+        card_height = cell.fetch(:height)
+        x = cell.fetch(:x)
+        card_y = cell.fetch(:y)
+        image = item["image"].to_s.sub(%r{\Aassets/}, "")
+        date = item.fetch("date")
+        kind = item.fetch("kind")
+        kind_color = kind_colors.fetch(kind, group.fetch(:accent))
+        date_width = [date.length * 7.3 + 22, 66].max.round
+        kind_width = [[kind.length * 6.9 + 24, 58].max.round, card_width - date_width - 25].min
+        max_name_chars = [[(card_width / 7.7).floor, 15].max, 30].min
+        max_note_chars = [[(card_width / 5.9).floor, 20].max, 48].min
+        name_lines = wrap_text(item.fetch("name"), max_chars: max_name_chars, max_lines: 2)
+        note_y = image_height + 66 + (name_lines.length * 19) + 9
+        note_lines = wrap_text(item.fetch("note"), max_chars: max_note_chars, max_lines: 2)
+        <<~CARD
+          <a href="#{html_escape(item.fetch("url"))}" target="_blank">
+            <g class="milestone-card" transform="translate(#{format('%.1f', x)} #{card_y})">
+              <rect class="card-bg" width="#{card_width}" height="#{card_height}" rx="14"/>
+              <rect class="image-frame" x="12" y="12" width="#{card_width - 24}" height="#{image_height}" rx="10"/>
+              <image href="#{html_escape(image)}" x="12" y="12" width="#{card_width - 24}" height="#{image_height}" preserveAspectRatio="xMidYMid meet"/>
+              <rect class="date-pill" x="12" y="#{image_height + 23}" width="#{date_width}" height="25" rx="12.5"/>
+              <text class="pill-text" x="#{12 + (date_width / 2.0)}" y="#{image_height + 40}" text-anchor="middle">#{html_escape(date)}</text>
+              <rect class="kind-pill" x="#{19 + date_width}" y="#{image_height + 23}" width="#{kind_width}" height="25" rx="12.5" fill="#{kind_color}"/>
+              <text class="pill-text" x="#{19 + date_width + (kind_width / 2.0)}" y="#{image_height + 40}" text-anchor="middle">#{html_escape(kind)}</text>
+              #{svg_text_block(name_lines, x: 12, y: image_height + 66, class_name: "card-title", line_height: 19)}
+              #{svg_text_block(note_lines, x: 12, y: note_y, class_name: "card-note", line_height: 15)}
+            </g>
+          </a>
+        CARD
+      end.join("\n")
+      label_lines = wrap_text(group.fetch(:theme), max_chars: 30, max_lines: 3)
+      <<~BAND
+        <g class="era-band" transform="translate(#{margin_x} #{y})">
+          <rect class="era-bg" width="#{row_width}" height="#{group.fetch(:row_height)}" rx="22"/>
+          <circle class="era-dot" cx="#{era_dot_x}" cy="54" r="14" fill="#{group.fetch(:color)}"/>
+          <path class="era-rail" d="M #{card_area_x - margin_x} 54 H #{row_width - 34}"/>
+          <text class="era-kicker" x="30" y="36">ERA #{group.fetch(:index) + 1}</text>
+          <text class="era-range" x="30" y="76">#{html_escape(group.fetch(:range))}</text>
+          <text class="era-label" x="30" y="111">#{html_escape(group.fetch(:label))}</text>
+          #{svg_text_block(label_lines, x: 30, y: 142, class_name: "era-copy", line_height: 17)}
         </g>
+        #{cards}
+      BAND
     end.join("\n")
+    spine_path = if era_nodes.length > 1
+      coords = era_nodes.map.with_index do |(x, y), index|
+        "#{index.zero? ? 'M' : 'L'} #{x} #{y}"
+      end.join(" ")
+      %(<path class="spine" d="#{coords}"/>)
+    else
+      ""
+    end
     <<~SVG
       <svg xmlns="http://www.w3.org/2000/svg" width="1280" height="#{height}" viewBox="0 0 1280 #{height}" role="img" aria-labelledby="title desc">
         <title id="title">Representative egocentric AI milestones</title>
+        <desc id="desc">A generated milestone poster for Awesome Egocentric Atlas, showing #{items.length} representative field-defining works from #{year_span}, grouped into era bands with uncropped visual panels.</desc>
         <defs>
           <linearGradient id="bg" x1="0" y1="0" x2="1" y2="1">
             <stop offset="0" stop-color="#fbfdff"/>
             <stop offset="0.56" stop-color="#f4faf9"/>
             <stop offset="1" stop-color="#eaf6f5"/>
           </linearGradient>
+          <linearGradient id="heroGlow" x1="0" y1="0" x2="1" y2="0">
+            <stop offset="0" stop-color="#0b8f98" stop-opacity=".20"/>
+            <stop offset=".48" stop-color="#ef9f24" stop-opacity=".16"/>
+            <stop offset="1" stop-color="#7a6ff0" stop-opacity=".16"/>
+          </linearGradient>
           <pattern id="dots" width="28" height="28" patternUnits="userSpaceOnUse">
             <circle cx="2" cy="2" r="1.35" fill="#cfe0e4" opacity="0.38"/>
           </pattern>
           </filter>
           <style>
             .kicker { font: 800 15px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #0b8f98; letter-spacing: .18em; }
+            .title { font: 850 46px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #14212b; letter-spacing: 0; }
             .subtitle { font: 500 18px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #53606b; letter-spacing: 0; }
             .stat-num { font: 900 43px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #0f3b45; letter-spacing: 0; }
             .stat-range { font: 900 34px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #0f3b45; letter-spacing: 0; }
             .stat-label { font: 700 14px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #61717b; letter-spacing: .08em; }
+            .hero-card { fill: #ffffff; fill-opacity: .70; stroke: #c9dde2; stroke-width: 1.2; }
+            .era-bg { fill: #ffffff; fill-opacity: .92; stroke: #c9dde2; stroke-width: 1.25; filter: url(#softShadow); }
+            .era-rail { stroke: #c8dde2; stroke-width: 1.35; stroke-dasharray: 6 8; }
+            .era-dot { stroke: #ffffff; stroke-width: 4; filter: url(#softShadow); }
+            .era-kicker { font: 850 12px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #0b8f98; letter-spacing: .16em; }
+            .era-range { font: 900 27px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #122733; letter-spacing: 0; }
+            .era-label { font: 850 18px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #263642; letter-spacing: 0; }
+            .era-copy { font: 560 12.2px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #62727c; letter-spacing: 0; }
+            .spine { fill: none; stroke: #0b8f98; stroke-width: 4; stroke-linecap: round; stroke-linejoin: round; opacity: .34; }
+            .card-bg { fill: #ffffff; fill-opacity: .97; stroke: #cfe0e4; stroke-width: 1.1; }
+            .milestone-card:hover .card-bg { stroke: #0b8f98; }
+            .image-frame { fill: #eef6f5; stroke: #d7e5e8; stroke-width: 1; }
             .date-pill { fill: #0b8f98; }
             .kind-pill { fill: #ef9f24; }
+            .pill-text { font: 800 10.8px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #ffffff; letter-spacing: 0; }
+            .card-title { font: 820 15px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #182733; letter-spacing: 0; }
+            .card-note { font: 520 11.4px system-ui, -apple-system, "Segoe UI", sans-serif; fill: #5c6b74; letter-spacing: 0; }
             .rule { stroke: #c8dde2; stroke-width: 1.4; }
           </style>
         </defs>
         <rect width="1280" height="#{height}" fill="url(#bg)"/>
         <rect width="1280" height="#{height}" fill="url(#dots)"/>
+        <rect class="hero-card" x="40" y="24" width="1200" height="138" rx="24"/>
+        <rect x="40" y="24" width="1200" height="138" rx="24" fill="url(#heroGlow)"/>
+        <line class="rule" x1="40" y1="150" x2="1240" y2="150"/>
+        <text class="kicker" x="40" y="48">CURATED FIELD MILESTONES</text>
+        <text class="title" x="40" y="101">Egocentric AI timeline</text>
+        <text class="subtitle" x="40" y="136">Representative works grouped by the shifts they created: egocentric data, scale, reasoning, robotics, and world models.</text>
         <text class="stat-range" x="1014" y="82" text-anchor="middle">#{year_span}</text>
         <text class="stat-label" x="1014" y="106" text-anchor="middle">SPAN</text>
         <text class="stat-num" x="1172" y="82" text-anchor="middle">#{items.length}</text>
         <text class="stat-label" x="1172" y="106" text-anchor="middle">WORKS</text>
+        #{spine_path}
+      #{bands}
       </svg>
     SVG
   end

scripts/verify_hf_mirror.rb ADDED Viewed

	@@ -0,0 +1,76 @@

+#!/usr/bin/env ruby
+# frozen_string_literal: true
+# Verifies that the public Hugging Face mirror exposes the key generated signals
+# from a locally built HF package.
+#
+# Usage:
+#   ruby scripts/verify_hf_mirror.rb hf-upload cy0307/awesome-egocentric-atlas
+require "json"
+require "open-uri"
+package_dir = ARGV[0] || "hf-upload"
+repo_id = ARGV[1] || ENV.fetch("HF_REPO_ID", "cy0307/awesome-egocentric-atlas")
+repo_type = ENV.fetch("HF_REPO_TYPE", "dataset")
+unless File.directory?(package_dir)
+  warn "HF package directory is missing: #{package_dir}"
+  exit 1
+end
+prefix = case repo_type
+when "dataset"
+  "datasets/"
+when "space"
+  "spaces/"
+else
+  ""
+end
+base = "https://huggingface.co/#{prefix}#{repo_id}/resolve/main"
+def fetch_text(url, attempts: 5)
+  last_error = nil
+  attempts.times do |index|
+    return URI.open(url, "Cache-Control" => "no-cache").read
+  rescue OpenURI::HTTPError, SocketError, SystemCallError, Timeout::Error => e
+    last_error = e
+    sleep(3 * (index + 1))
+  end
+  warn "Could not fetch #{url}: #{last_error.class}: #{last_error.message}"
+  exit 1
+end
+local_site = JSON.parse(File.read(File.join(package_dir, "site-data.json"), encoding: "UTF-8"))
+remote_site = JSON.parse(fetch_text("#{base}/site-data.json?download=1&ts=#{Time.now.to_i}"))
+local_summary = local_site.fetch("summary")
+remote_summary = remote_site.fetch("summary")
+%w[egocentric_resources total_resources adjacent_resources].each do |key|
+  next if remote_summary[key] == local_summary[key]
+  warn "HF site-data summary mismatch for #{key}: local=#{local_summary[key].inspect}, remote=#{remote_summary[key].inspect}"
+  exit 1
+end
+local_date = local_site.fetch("meta").fetch("last_major_audit")
+remote_date = remote_site.fetch("meta").fetch("last_major_audit")
+if remote_date != local_date
+  warn "HF site-data audit date mismatch: local=#{local_date.inspect}, remote=#{remote_date.inspect}"
+  exit 1
+end
+remote_readme = fetch_text("#{base}/README.md?download=1&ts=#{Time.now.to_i}")
+required = [
+  "A curated map of egocentric AI",
+  "**Updated:** #{local_date}.",
+  "badge/resources-#{local_summary.fetch('egocentric_resources')}-"
+]
+missing = required.reject { |needle| remote_readme.include?(needle) }
+unless missing.empty?
+  warn "HF README is missing expected signal(s): #{missing.join(', ')}"
+  exit 1
+end
+puts "Hugging Face mirror OK: #{repo_id} (#{local_summary.fetch('egocentric_resources')} egocentric resources, #{local_date})"

site-data.json CHANGED Viewed

@@ -1,7 +1,7 @@
 {
   "meta": {
     "title": "Awesome Egocentric Atlas",
-    "description": "Just launched Awesome Egocentric Atlas — a curated collection of first-person AI resources for egocentric vision, embodied AI, robotics, VLA, world models, WMA, memory, AR/VR, and hand-object interaction.",
     "last_major_audit": "2026-06-20",
     "status_legend": {
       "open": "Public download, public annotations, public code, or application-based access is clearly documented.",
@@ -106,8 +106,8 @@
       "name": "CMU-MMAC",
       "kind": "dataset",
       "url": "http://kitchen.cs.cmu.edu/",
-      "date": "2009",
-      "note": "The earliest egocentric dataset; launched first-person activity recognition at the first IEEE Workshop on Egocentric Vision (CVPR 2009).",
       "image": "assets/milestones/cmu-mmac.png"
     },
     {
@@ -115,22 +115,22 @@
       "kind": "dataset",
       "url": "https://cbs.ic.gatech.edu/fpv/",
       "date": "2011-06",
-      "note": "Foundational hand-object and gaze egocentric activity datasets (GTEA, CVPR 2011) that seeded first-person action and attention research.",
       "image": "assets/milestones/gtea-gaze.png"
     },
     {
       "name": "ADL Dataset",
       "kind": "dataset",
       "url": "https://www.csc.kth.se/cvap/actions/",
-      "date": "2012",
-      "note": "Classic unscripted daily-life egocentric activity corpus with object and hand annotations; the reference point for ADL-scale first-person recognition.",
       "image": "assets/milestones/adl-dataset.png"
     },
     {
       "name": "EgoHands",
       "kind": "dataset",
       "url": "http://vision.soic.indiana.edu/projects/egohands/",
-      "date": "2015",
       "note": "The Google Glass hand-segmentation benchmark that made hands a first-class signal for egocentric perception.",
       "image": "assets/milestones/egohands.png"
     },
@@ -150,14 +150,6 @@
       "note": "The 3,670-hour massive-scale benchmark suite that catalyzed the modern egocentric era.",
       "image": "assets/milestones/ego4d.png"
     },
-    {
-      "name": "Project Aria Datasets",
-      "kind": "collection",
-      "url": "https://www.projectaria.com/datasets/",
-      "date": "2022",
-      "note": "Meta's research smart-glasses platform that opened the modern wave of AR and wearable egocentric data.",
-      "image": "assets/milestones/project-aria.png"
-    },
     {
       "name": "HOI4D",
       "kind": "dataset",
@@ -182,6 +174,14 @@
       "note": "The benchmark that exposed how far models are from long-form egocentric video reasoning.",
       "image": "assets/milestones/egoschema.png"
     },
     {
       "name": "Ego-Exo4D",
       "kind": "dataset",
@@ -222,6 +222,14 @@
       "note": "Showed vision-language-action policies can be learned from egocentric human video and transferred to robots.",
       "image": "assets/milestones/egovla.png"
     },
     {
       "name": "EgoScale",
       "kind": "dataset",
@@ -235,10 +243,196 @@
       "kind": "dataset",
       "url": "https://huggingface.co/datasets/ropedia-ai/xperience-10m",
       "date": "2026-03",
-      "note": "Petascale egocentric world-model corpus (10M experiences, ~1 PB) pushing first-person data to internet scale for embodied AI and robot learning.",
       "image": "assets/milestones/xperience-10m.png"
     }
   ],
   "resources": [
     {
       "name": "Xperience-10M",
@@ -281,7 +475,7 @@
     {
       "name": "Xperience-10M Sample",
       "kind": "dataset",
-      "released": "2026",
       "venue": "Hugging Face",
       "year": 2026,
       "status": "open",
@@ -1158,7 +1352,7 @@
     {
       "name": "EgoHands",
       "kind": "dataset",
-      "released": "2015",
       "venue": "ICCV 2015",
       "year": 2015,
       "status": "open",
@@ -1182,12 +1376,13 @@
     {
       "name": "FPHA",
       "kind": "dataset",
-      "released": "2017",
       "venue": "CVPR 2018",
       "year": 2017,
       "status": "open",
       "scope": "egocentric",
       "url": "https://guiggh.github.io/publications/first-person-hands/",
       "scale": "100K+ RGB-D frames, 45 action classes, 26 objects",
       "tasks": [
         "hand-action-recognition",
@@ -1280,12 +1475,13 @@
     {
       "name": "EgoBody",
       "kind": "dataset",
-      "released": "2022",
       "venue": "ECCV 2022",
-      "year": 2022,
       "status": "open",
       "scope": "egocentric",
       "url": "https://sanweiliti.github.io/egobody/egobody.html",
       "tasks": [
         "3d-human-pose",
         "body-shape",
@@ -1388,12 +1584,13 @@
     {
       "name": "xR-EgoPose",
       "kind": "dataset",
-      "released": "2019",
       "venue": "GitHub",
       "year": 2019,
       "status": "open",
       "scope": "egocentric",
       "url": "https://github.com/facebookresearch/xR-EgoPose",
       "tasks": [
         "xr-pose-estimation"
       ],
@@ -1454,12 +1651,13 @@
     {
       "name": "TREK-150",
       "kind": "benchmark",
-      "released": "2021",
       "venue": "project page",
       "year": 2021,
       "status": "open",
       "scope": "egocentric",
       "url": "https://machinelearning.uniud.it/datasets/trek150/",
       "tasks": [
         "single-object-tracking"
       ],
@@ -1470,12 +1668,13 @@
     {
       "name": "Project Aria Datasets",
       "kind": "collection",
-      "released": "2022",
-      "venue": "project page",
-      "year": 2022,
       "status": "open",
       "scope": "egocentric",
       "url": "https://www.projectaria.com/datasets/",
       "tasks": [
         "ar-perception",
         "scene-understanding",
@@ -2548,12 +2747,13 @@
     {
       "name": "VISOR",
       "kind": "benchmark",
-      "released": "2022",
       "venue": "NeurIPS 2022",
       "year": 2022,
       "status": "open",
       "scope": "egocentric",
       "url": "https://epic-kitchens.github.io/VISOR/",
       "tasks": [
         "hand-segmentation",
         "active-object-segmentation",
@@ -2568,12 +2768,13 @@
     {
       "name": "EPIC-Sounds",
       "kind": "benchmark",
-      "released": "2023",
       "venue": "ICASSP 2023",
       "year": 2023,
       "status": "open",
       "scope": "egocentric",
       "url": "https://epic-kitchens.github.io/epic-sounds/",
       "tasks": [
         "audio-event-recognition"
       ],
@@ -2584,12 +2785,13 @@
     {
       "name": "EPIC-Fields",
       "kind": "benchmark",
-      "released": "2023",
       "venue": "NeurIPS 2023",
       "year": 2023,
       "status": "open",
       "scope": "egocentric",
       "url": "https://epic-kitchens.github.io/epic-fields/",
       "tasks": [
         "3d-fields",
         "spatial-reasoning"
@@ -2733,7 +2935,7 @@
     {
       "name": "Ropedia Xperience-10M Task Suite",
       "kind": "benchmark",
-      "released": "2026",
       "venue": "Hugging Face",
       "year": 2026,
       "status": "open",
@@ -2763,7 +2965,7 @@
     {
       "name": "Ropedia Xperience-10M Task Baselines",
       "kind": "model",
-      "released": "2026",
       "venue": "Hugging Face",
       "year": 2026,
       "status": "open",
@@ -2921,12 +3123,13 @@
     {
       "name": "Project Aria Tools",
       "kind": "toolkit",
-      "released": "2022",
-      "venue": "GitHub",
-      "year": 2022,
       "status": "open",
       "scope": "egocentric",
       "url": "https://github.com/facebookresearch/projectaria_tools",
       "tasks": [
         "vrs-loading",
         "calibration",
@@ -2957,7 +3160,7 @@
     {
       "name": "HOMIE-toolkit",
       "kind": "toolkit",
-      "released": "2026",
       "venue": "GitHub",
       "year": 2026,
       "status": "open",
@@ -4218,12 +4421,13 @@
     {
       "name": "EgoCom / Ego audio-visual correspondence",
       "kind": "dataset",
-      "released": "2020",
-      "venue": "project page",
-      "year": 2020,
       "status": "open",
       "scope": "egocentric",
       "url": "http://vision.cs.utexas.edu/projects/ego_av_corr/",
       "scale": "Egocentric video with spatial audio for conversation and audio-visual correspondence tasks",
       "tasks": [
         "foundation-video",
@@ -4279,7 +4483,7 @@
     {
       "name": "EgoVLA",
       "kind": "model",
-      "released": "2025",
       "venue": "project page",
       "status": "watch",
       "scope": "egocentric",
@@ -4439,11 +4643,13 @@
     {
       "name": "EgoEVHands",
       "kind": "dataset",
-      "released": "2024",
-      "venue": "GitHub",
       "status": "watch",
       "scope": "egocentric",
       "url": "https://github.com/ZJUWang01/EgoEV-HandPose",
       "scale": "Stereo event-camera egocentric hand dataset with 5,419 sequences and 3D/2D keypoints",
       "tasks": [
         "hand-object",
@@ -4522,11 +4728,13 @@
     {
       "name": "EgoSelf",
       "kind": "model",
-      "released": "2025",
-      "venue": "project page",
       "status": "watch",
       "scope": "egocentric",
       "url": "https://abie-e.github.io/egoself_project/",
       "scale": "Personalized egocentric assistant framework with graph memory",
       "tasks": [
         "memory",
@@ -4542,11 +4750,13 @@
     {
       "name": "EgoCross",
       "kind": "dataset",
-      "released": "2025",
-      "venue": "GitHub",
       "status": "watch",
       "scope": "egocentric",
       "url": "https://github.com/MyUniverse0726/EgoCross",
       "scale": "About 1,000 QA pairs over surgery, industry, extreme sports, and animal-perspective clips",
       "tasks": [
         "memory",
@@ -4562,7 +4772,7 @@
     {
       "name": "ADL Dataset",
       "kind": "dataset",
-      "released": "2012",
       "venue": "project page",
       "year": 2012,
       "status": "partial",
@@ -4600,12 +4810,13 @@
     {
       "name": "Visual Experience Dataset / VEDB",
       "kind": "dataset",
-      "released": "2023",
-      "venue": "project page",
-      "year": 2023,
       "status": "partial",
       "scope": "egocentric",
       "url": "http://tamaraberg.com/visualexperience/",
       "scale": "240+ hours egocentric video with gaze/head tracking in classic literature",
       "tasks": [
         "action-recognition",
@@ -4618,7 +4829,7 @@
     {
       "name": "UT Ego",
       "kind": "dataset",
-      "released": "2012",
       "venue": "project page",
       "year": 2012,
       "status": "partial",
@@ -4636,7 +4847,7 @@
     {
       "name": "HUJI EgoSeg",
       "kind": "dataset",
-      "released": "2014",
       "venue": "project page",
       "year": 2014,
       "status": "partial",
@@ -4672,12 +4883,13 @@
     {
       "name": "FT-HID",
       "kind": "dataset",
-      "released": "2022",
       "venue": "GitHub",
       "year": 2022,
       "status": "open",
       "scope": "egocentric",
       "url": "https://github.com/ENDLICHERE/FT-HID",
       "scale": "90K+ RGB-D first- and third-person human interaction samples from 109 subjects",
       "tasks": [
         "action-recognition",
@@ -4734,7 +4946,7 @@
     {
       "name": "Ego4D Benchmarks",
       "kind": "benchmark",
-      "released": "2021",
       "venue": "project page",
       "year": 2021,
       "status": "benchmark",
@@ -4752,7 +4964,7 @@
     {
       "name": "Ego-Exo4D Benchmarks",
       "kind": "benchmark",
-      "released": "2023",
       "venue": "project page",
       "year": 2023,
       "status": "benchmark",
@@ -4770,7 +4982,7 @@
     {
       "name": "EPIC-KITCHENS Challenges",
       "kind": "benchmark",
-      "released": "2018",
       "venue": "project page",
       "year": 2018,
       "status": "benchmark",
@@ -4825,11 +5037,13 @@
     {
       "name": "EgoMAS",
       "kind": "model",
-      "released": "2026",
-      "venue": "project page",
       "status": "open",
       "scope": "egocentric",
       "url": "https://ma-egoqa.github.io/",
       "scale": "Shared-memory baseline for multi-agent egocentric video QA",
       "tasks": [
         "video-language",
@@ -4866,11 +5080,13 @@
     {
       "name": "EgoPoseFormer",
       "kind": "model",
-      "released": "2024",
-      "venue": "GitHub",
       "status": "open",
       "scope": "egocentric",
       "url": "https://github.com/ChenhongyiYang/egoposeformer",
       "scale": "Transformer baseline for stereo egocentric 3D human pose estimation",
       "tasks": [
         "action",
@@ -4911,12 +5127,13 @@
     {
       "name": "EgoHOS model",
       "kind": "model",
-      "released": "2022",
       "venue": "GitHub",
       "year": 2022,
       "status": "open",
       "scope": "egocentric",
       "url": "https://github.com/owenzlz/EgoHOS",
       "scale": "Context-aware hand-object segmentation and augmentation pipeline",
       "tasks": [
         "action",
@@ -4934,12 +5151,13 @@
     {
       "name": "AV-CONV",
       "kind": "model",
-      "released": "2023",
-      "venue": "project page",
       "year": 2023,
       "status": "open",
       "scope": "egocentric",
       "url": "https://vjwq.github.io/AV-CONV/",
       "scale": "Audio-visual conversational graph prediction from ego/exo conversation",
       "tasks": [
         "action",
@@ -5003,7 +5221,7 @@
     {
       "name": "Ego4D CLI and docs",
       "kind": "toolkit",
-      "released": "2021",
       "venue": "project page",
       "year": 2021,
       "status": "open",
@@ -5020,7 +5238,7 @@
     {
       "name": "Ego-Exo4D CLI and docs",
       "kind": "toolkit",
-      "released": "2023",
       "venue": "project page",
       "year": 2023,
       "status": "open",
@@ -5037,7 +5255,7 @@
     {
       "name": "VISOR API",
       "kind": "toolkit",
-      "released": "2022",
       "venue": "GitHub",
       "year": 2022,
       "status": "open",
@@ -5054,12 +5272,13 @@
     {
       "name": "EgoObjects API",
       "kind": "toolkit",
-      "released": "2023",
-      "venue": "GitHub",
       "year": 2023,
       "status": "open",
       "scope": "egocentric",
       "url": "https://github.com/facebookresearch/EgoObjects",
       "scale": "Working with category and instance-level egocentric object labels.",
       "tasks": [
         "tooling"
@@ -5071,7 +5290,7 @@
     {
       "name": "HOT3D tooling",
       "kind": "toolkit",
-      "released": "2024",
       "venue": "project page",
       "status": "open",
       "scope": "egocentric",
@@ -5087,7 +5306,7 @@
     {
       "name": "HOI4D tooling",
       "kind": "toolkit",
-      "released": "2022",
       "venue": "project page",
       "year": 2022,
       "status": "open",
@@ -5252,9 +5471,9 @@
     {
       "name": "EgoMemory",
       "kind": "benchmark",
-      "released": "2026",
       "venue": "OpenReview",
-      "year": 2026,
       "status": "watch",
       "scope": "egocentric",
       "url": "https://openreview.net/forum?id=T0em4hJCQb",
@@ -5271,7 +5490,7 @@
     {
       "name": "EgoTextVQA",
       "kind": "benchmark",
-      "released": "2025",
       "venue": "CVPR 2025",
       "year": 2025,
       "status": "open",
@@ -5312,7 +5531,7 @@
     {
       "name": "EgoVQA",
       "kind": "benchmark",
-      "released": "2019",
       "venue": "ICCV 2019",
       "year": 2019,
       "status": "open",
@@ -6226,9 +6445,9 @@
     {
       "name": "EgoSurgery",
       "kind": "dataset",
-      "released": "2024",
-      "venue": "MICCAI 2024",
-      "year": 2024,
       "status": "open",
       "scope": "egocentric",
       "url": "https://github.com/Fujiry0/EgoSurgery",
@@ -8095,7 +8314,7 @@
     {
       "name": "CMU-MMAC",
       "kind": "dataset",
-      "released": "2009",
       "venue": "CMU tech report 2009",
       "year": 2009,
       "status": "open",
@@ -8123,7 +8342,7 @@
     {
       "name": "First-Person Social Interactions",
       "kind": "dataset",
-      "released": "2012",
       "venue": "CVPR 2012",
       "year": 2012,
       "status": "open",
@@ -8150,7 +8369,7 @@
     {
       "name": "BEOID",
       "kind": "dataset",
-      "released": "2014",
       "venue": "BMVC 2014",
       "year": 2014,
       "status": "open",
@@ -11981,7 +12200,7 @@
     {
       "name": "First-Person Pose Recognition",
       "kind": "model",
-      "released": "2015",
       "venue": "CVPR 2015",
       "year": 2015,
       "status": "watch",

 {
   "meta": {
     "title": "Awesome Egocentric Atlas",
+    "description": "Just launched Awesome Egocentric Atlas — a curated collection of egocentric AI resources for egocentric vision, embodied AI, robotics, VLA, world models, WMA, memory, AR/VR, and hand-object interaction.",
     "last_major_audit": "2026-06-20",
     "status_legend": {
       "open": "Public download, public annotations, public code, or application-based access is clearly documented.",
       "name": "CMU-MMAC",
       "kind": "dataset",
       "url": "http://kitchen.cs.cmu.edu/",
+      "date": "2009-06",
+      "note": "The earliest egocentric dataset; launched egocentric activity recognition at the first IEEE Workshop on Egocentric Vision (CVPR 2009).",
       "image": "assets/milestones/cmu-mmac.png"
     },
     {
       "kind": "dataset",
       "url": "https://cbs.ic.gatech.edu/fpv/",
       "date": "2011-06",
+      "note": "Foundational hand-object and gaze egocentric activity datasets (GTEA, CVPR 2011) that seeded egocentric action and attention research.",
       "image": "assets/milestones/gtea-gaze.png"
     },
     {
       "name": "ADL Dataset",
       "kind": "dataset",
       "url": "https://www.csc.kth.se/cvap/actions/",
+      "date": "2012-06",
+      "note": "Classic unscripted daily-life egocentric activity corpus with object and hand annotations; the reference point for ADL-scale egocentric recognition.",
       "image": "assets/milestones/adl-dataset.png"
     },
     {
       "name": "EgoHands",
       "kind": "dataset",
       "url": "http://vision.soic.indiana.edu/projects/egohands/",
+      "date": "2015-12",
       "note": "The Google Glass hand-segmentation benchmark that made hands a first-class signal for egocentric perception.",
       "image": "assets/milestones/egohands.png"
     },
       "note": "The 3,670-hour massive-scale benchmark suite that catalyzed the modern egocentric era.",
       "image": "assets/milestones/ego4d.png"
     },
     {
       "name": "HOI4D",
       "kind": "dataset",
       "note": "The benchmark that exposed how far models are from long-form egocentric video reasoning.",
       "image": "assets/milestones/egoschema.png"
     },
+    {
+      "name": "Project Aria Datasets",
+      "kind": "collection",
+      "url": "https://www.projectaria.com/datasets/",
+      "date": "2023-08",
+      "note": "Meta's research smart-glasses platform that opened the modern wave of AR and wearable egocentric data.",
+      "image": "assets/milestones/project-aria.png"
+    },
     {
       "name": "Ego-Exo4D",
       "kind": "dataset",
       "note": "Showed vision-language-action policies can be learned from egocentric human video and transferred to robots.",
       "image": "assets/milestones/egovla.png"
     },
+    {
+      "name": "DreamDojo",
+      "kind": "model",
+      "url": "https://github.com/NVIDIA/DreamDojo",
+      "date": "2026-02",
+      "note": "44K-hour egocentric-video robot world model with latent actions for real-time planning.",
+      "image": "assets/milestones/dreamdojo.png"
+    },
     {
       "name": "EgoScale",
       "kind": "dataset",
       "kind": "dataset",
       "url": "https://huggingface.co/datasets/ropedia-ai/xperience-10m",
       "date": "2026-03",
+      "note": "Petascale egocentric world-model corpus (10M experiences, ~1 PB) pushing egocentric data to internet scale for embodied AI and robot learning.",
       "image": "assets/milestones/xperience-10m.png"
     }
   ],
+  "milestone_layout": {
+    "width": 1280,
+    "height": 2322,
+    "cells": [
+      {
+        "name": "CMU-MMAC",
+        "date": "2009-06",
+        "kind": "dataset",
+        "url": "http://kitchen.cs.cmu.edu/",
+        "x": 266.0,
+        "y": 266,
+        "width": 228,
+        "height": 295
+      },
+      {
+        "name": "GTEA / GTEA Gaze / EGTEA Gaze+",
+        "date": "2011-06",
+        "kind": "dataset",
+        "url": "https://cbs.ic.gatech.edu/fpv/",
+        "x": 508.0,
+        "y": 266,
+        "width": 228,
+        "height": 295
+      },
+      {
+        "name": "ADL Dataset",
+        "date": "2012-06",
+        "kind": "dataset",
+        "url": "https://www.csc.kth.se/cvap/actions/",
+        "x": 750.0,
+        "y": 266,
+        "width": 228,
+        "height": 295
+      },
+      {
+        "name": "EgoHands",
+        "date": "2015-12",
+        "kind": "dataset",
+        "url": "http://vision.soic.indiana.edu/projects/egohands/",
+        "x": 992.0,
+        "y": 266,
+        "width": 228,
+        "height": 295
+      },
+      {
+        "name": "EPIC-KITCHENS-100",
+        "date": "2020-06",
+        "kind": "dataset",
+        "url": "https://epic-kitchens.github.io/",
+        "x": 266.0,
+        "y": 691,
+        "width": 228,
+        "height": 295
+      },
+      {
+        "name": "Ego4D",
+        "date": "2021-10",
+        "kind": "dataset",
+        "url": "https://ego4d-data.org/",
+        "x": 508.0,
+        "y": 691,
+        "width": 228,
+        "height": 295
+      },
+      {
+        "name": "HOI4D",
+        "date": "2022-03",
+        "kind": "dataset",
+        "url": "https://hoi4d.github.io/",
+        "x": 750.0,
+        "y": 691,
+        "width": 228,
+        "height": 295
+      },
+      {
+        "name": "EgoVLP",
+        "date": "2022-06",
+        "kind": "model",
+        "url": "https://github.com/showlab/EgoVLP",
+        "x": 992.0,
+        "y": 691,
+        "width": 228,
+        "height": 295
+      },
+      {
+        "name": "EgoSchema",
+        "date": "2023-08",
+        "kind": "benchmark",
+        "url": "http://egoschema.github.io/",
+        "x": 267.5,
+        "y": 1116,
+        "width": 179,
+        "height": 265
+      },
+      {
+        "name": "Project Aria Datasets",
+        "date": "2023-08",
+        "kind": "collection",
+        "url": "https://www.projectaria.com/datasets/",
+        "x": 460.5,
+        "y": 1116,
+        "width": 179,
+        "height": 265
+      },
+      {
+        "name": "Ego-Exo4D",
+        "date": "2023-11",
+        "kind": "dataset",
+        "url": "https://ego-exo4d-data.org/",
+        "x": 653.5,
+        "y": 1116,
+        "width": 179,
+        "height": 265
+      },
+      {
+        "name": "Universal Manipulation Interface / UMI",
+        "date": "2024-02",
+        "kind": "toolkit",
+        "url": "https://umi-gripper.github.io/",
+        "x": 846.5,
+        "y": 1116,
+        "width": 179,
+        "height": 265
+      },
+      {
+        "name": "HOT3D",
+        "date": "2024-06",
+        "kind": "dataset",
+        "url": "https://facebookresearch.github.io/hot3d/",
+        "x": 1039.5,
+        "y": 1116,
+        "width": 179,
+        "height": 265
+      },
+      {
+        "name": "EgoLife",
+        "date": "2025-03",
+        "kind": "dataset",
+        "url": "https://arxiv.org/abs/2503.03803",
+        "x": 450.0,
+        "y": 1511,
+        "width": 286,
+        "height": 312
+      },
+      {
+        "name": "EgoVLA",
+        "date": "2025-07",
+        "kind": "model",
+        "url": "https://rchalyang.github.io/EgoVLA/",
+        "x": 750.0,
+        "y": 1511,
+        "width": 286,
+        "height": 312
+      },
+      {
+        "name": "DreamDojo",
+        "date": "2026-02",
+        "kind": "model",
+        "url": "https://github.com/NVIDIA/DreamDojo",
+        "x": 300.0,
+        "y": 1953,
+        "width": 286,
+        "height": 312
+      },
+      {
+        "name": "EgoScale",
+        "date": "2026-02",
+        "kind": "dataset",
+        "url": "https://arxiv.org/abs/2602.16710",
+        "x": 600.0,
+        "y": 1953,
+        "width": 286,
+        "height": 312
+      },
+      {
+        "name": "Xperience-10M",
+        "date": "2026-03",
+        "kind": "dataset",
+        "url": "https://huggingface.co/datasets/ropedia-ai/xperience-10m",
+        "x": 900.0,
+        "y": 1953,
+        "width": 286,
+        "height": 312
+      }
+    ]
+  },
   "resources": [
     {
       "name": "Xperience-10M",
     {
       "name": "Xperience-10M Sample",
       "kind": "dataset",
+      "released": "2026-03",
       "venue": "Hugging Face",
       "year": 2026,
       "status": "open",
     {
       "name": "EgoHands",
       "kind": "dataset",
+      "released": "2015-12",
       "venue": "ICCV 2015",
       "year": 2015,
       "status": "open",
     {
       "name": "FPHA",
       "kind": "dataset",
+      "released": "2017-04",
       "venue": "CVPR 2018",
       "year": 2017,
       "status": "open",
       "scope": "egocentric",
       "url": "https://guiggh.github.io/publications/first-person-hands/",
+      "paper": "https://arxiv.org/abs/1704.02463",
       "scale": "100K+ RGB-D frames, 45 action classes, 26 objects",
       "tasks": [
         "hand-action-recognition",
     {
       "name": "EgoBody",
       "kind": "dataset",
+      "released": "2021-12",
       "venue": "ECCV 2022",
+      "year": 2021,
       "status": "open",
       "scope": "egocentric",
       "url": "https://sanweiliti.github.io/egobody/egobody.html",
+      "paper": "https://arxiv.org/abs/2112.07642",
       "tasks": [
         "3d-human-pose",
         "body-shape",
     {
       "name": "xR-EgoPose",
       "kind": "dataset",
+      "released": "2019-07",
       "venue": "GitHub",
       "year": 2019,
       "status": "open",
       "scope": "egocentric",
       "url": "https://github.com/facebookresearch/xR-EgoPose",
+      "paper": "https://arxiv.org/abs/1907.10045",
       "tasks": [
         "xr-pose-estimation"
       ],
     {
       "name": "TREK-150",
       "kind": "benchmark",
+      "released": "2021-08",
       "venue": "project page",
       "year": 2021,
       "status": "open",
       "scope": "egocentric",
       "url": "https://machinelearning.uniud.it/datasets/trek150/",
+      "paper": "https://arxiv.org/abs/2108.13665",
       "tasks": [
         "single-object-tracking"
       ],
     {
       "name": "Project Aria Datasets",
       "kind": "collection",
+      "released": "2023-08",
+      "venue": "arXiv",
+      "year": 2023,
       "status": "open",
       "scope": "egocentric",
       "url": "https://www.projectaria.com/datasets/",
+      "paper": "https://arxiv.org/abs/2308.13561",
       "tasks": [
         "ar-perception",
         "scene-understanding",
     {
       "name": "VISOR",
       "kind": "benchmark",
+      "released": "2022-09",
       "venue": "NeurIPS 2022",
       "year": 2022,
       "status": "open",
       "scope": "egocentric",
       "url": "https://epic-kitchens.github.io/VISOR/",
+      "paper": "https://arxiv.org/abs/2209.08199",
       "tasks": [
         "hand-segmentation",
         "active-object-segmentation",
     {
       "name": "EPIC-Sounds",
       "kind": "benchmark",
+      "released": "2023-02",
       "venue": "ICASSP 2023",
       "year": 2023,
       "status": "open",
       "scope": "egocentric",
       "url": "https://epic-kitchens.github.io/epic-sounds/",
+      "paper": "https://arxiv.org/abs/2302.00646",
       "tasks": [
         "audio-event-recognition"
       ],
     {
       "name": "EPIC-Fields",
       "kind": "benchmark",
+      "released": "2023-06",
       "venue": "NeurIPS 2023",
       "year": 2023,
       "status": "open",
       "scope": "egocentric",
       "url": "https://epic-kitchens.github.io/epic-fields/",
+      "paper": "https://arxiv.org/abs/2306.08731",
       "tasks": [
         "3d-fields",
         "spatial-reasoning"
     {
       "name": "Ropedia Xperience-10M Task Suite",
       "kind": "benchmark",
+      "released": "2026-03",
       "venue": "Hugging Face",
       "year": 2026,
       "status": "open",
     {
       "name": "Ropedia Xperience-10M Task Baselines",
       "kind": "model",
+      "released": "2026-03",
       "venue": "Hugging Face",
       "year": 2026,
       "status": "open",
     {
       "name": "Project Aria Tools",
       "kind": "toolkit",
+      "released": "2023-08",
+      "venue": "arXiv",
+      "year": 2023,
       "status": "open",
       "scope": "egocentric",
       "url": "https://github.com/facebookresearch/projectaria_tools",
+      "paper": "https://arxiv.org/abs/2308.13561",
       "tasks": [
         "vrs-loading",
         "calibration",
     {
       "name": "HOMIE-toolkit",
       "kind": "toolkit",
+      "released": "2026-03",
       "venue": "GitHub",
       "year": 2026,
       "status": "open",
     {
       "name": "EgoCom / Ego audio-visual correspondence",
       "kind": "dataset",
+      "released": "2023-07",
+      "venue": "arXiv",
+      "year": 2023,
       "status": "open",
       "scope": "egocentric",
       "url": "http://vision.cs.utexas.edu/projects/ego_av_corr/",
+      "paper": "https://arxiv.org/abs/2307.04760",
       "scale": "Egocentric video with spatial audio for conversation and audio-visual correspondence tasks",
       "tasks": [
         "foundation-video",
     {
       "name": "EgoVLA",
       "kind": "model",
+      "released": "2025-07",
       "venue": "project page",
       "status": "watch",
       "scope": "egocentric",
     {
       "name": "EgoEVHands",
       "kind": "dataset",
+      "released": "2026-05",
+      "venue": "arXiv",
+      "year": 2026,
       "status": "watch",
       "scope": "egocentric",
       "url": "https://github.com/ZJUWang01/EgoEV-HandPose",
+      "paper": "https://arxiv.org/abs/2605.12297",
       "scale": "Stereo event-camera egocentric hand dataset with 5,419 sequences and 3D/2D keypoints",
       "tasks": [
         "hand-object",
     {
       "name": "EgoSelf",
       "kind": "model",
+      "released": "2026-04",
+      "venue": "arXiv",
+      "year": 2026,
       "status": "watch",
       "scope": "egocentric",
       "url": "https://abie-e.github.io/egoself_project/",
+      "paper": "https://arxiv.org/abs/2604.19564",
       "scale": "Personalized egocentric assistant framework with graph memory",
       "tasks": [
         "memory",
     {
       "name": "EgoCross",
       "kind": "dataset",
+      "released": "2025-08",
+      "venue": "arXiv",
+      "year": 2025,
       "status": "watch",
       "scope": "egocentric",
       "url": "https://github.com/MyUniverse0726/EgoCross",
+      "paper": "https://arxiv.org/abs/2508.10729",
       "scale": "About 1,000 QA pairs over surgery, industry, extreme sports, and animal-perspective clips",
       "tasks": [
         "memory",
     {
       "name": "ADL Dataset",
       "kind": "dataset",
+      "released": "2012-06",
       "venue": "project page",
       "year": 2012,
       "status": "partial",
     {
       "name": "Visual Experience Dataset / VEDB",
       "kind": "dataset",
+      "released": "2024-02",
+      "venue": "arXiv",
+      "year": 2024,
       "status": "partial",
       "scope": "egocentric",
       "url": "http://tamaraberg.com/visualexperience/",
+      "paper": "https://arxiv.org/abs/2404.18934",
       "scale": "240+ hours egocentric video with gaze/head tracking in classic literature",
       "tasks": [
         "action-recognition",
     {
       "name": "UT Ego",
       "kind": "dataset",
+      "released": "2012-06",
       "venue": "project page",
       "year": 2012,
       "status": "partial",
     {
       "name": "HUJI EgoSeg",
       "kind": "dataset",
+      "released": "2014-06",
       "venue": "project page",
       "year": 2014,
       "status": "partial",
     {
       "name": "FT-HID",
       "kind": "dataset",
+      "released": "2022-09",
       "venue": "GitHub",
       "year": 2022,
       "status": "open",
       "scope": "egocentric",
       "url": "https://github.com/ENDLICHERE/FT-HID",
+      "paper": "https://arxiv.org/abs/2209.10155",
       "scale": "90K+ RGB-D first- and third-person human interaction samples from 109 subjects",
       "tasks": [
         "action-recognition",
     {
       "name": "Ego4D Benchmarks",
       "kind": "benchmark",
+      "released": "2021-10",
       "venue": "project page",
       "year": 2021,
       "status": "benchmark",
     {
       "name": "Ego-Exo4D Benchmarks",
       "kind": "benchmark",
+      "released": "2023-11",
       "venue": "project page",
       "year": 2023,
       "status": "benchmark",
     {
       "name": "EPIC-KITCHENS Challenges",
       "kind": "benchmark",
+      "released": "2018-04",
       "venue": "project page",
       "year": 2018,
       "status": "benchmark",
     {
       "name": "EgoMAS",
       "kind": "model",
+      "released": "2026-03",
+      "venue": "arXiv",
+      "year": 2026,
       "status": "open",
       "scope": "egocentric",
       "url": "https://ma-egoqa.github.io/",
+      "paper": "https://arxiv.org/abs/2603.09827",
       "scale": "Shared-memory baseline for multi-agent egocentric video QA",
       "tasks": [
         "video-language",
     {
       "name": "EgoPoseFormer",
       "kind": "model",
+      "released": "2024-03",
+      "venue": "arXiv",
+      "year": 2024,
       "status": "open",
       "scope": "egocentric",
       "url": "https://github.com/ChenhongyiYang/egoposeformer",
+      "paper": "https://arxiv.org/abs/2403.18080",
       "scale": "Transformer baseline for stereo egocentric 3D human pose estimation",
       "tasks": [
         "action",
     {
       "name": "EgoHOS model",
       "kind": "model",
+      "released": "2022-08",
       "venue": "GitHub",
       "year": 2022,
       "status": "open",
       "scope": "egocentric",
       "url": "https://github.com/owenzlz/EgoHOS",
+      "paper": "https://arxiv.org/abs/2208.03826",
       "scale": "Context-aware hand-object segmentation and augmentation pipeline",
       "tasks": [
         "action",
     {
       "name": "AV-CONV",
       "kind": "model",
+      "released": "2023-12",
+      "venue": "arXiv",
       "year": 2023,
       "status": "open",
       "scope": "egocentric",
       "url": "https://vjwq.github.io/AV-CONV/",
+      "paper": "https://arxiv.org/abs/2312.12870",
       "scale": "Audio-visual conversational graph prediction from ego/exo conversation",
       "tasks": [
         "action",
     {
       "name": "Ego4D CLI and docs",
       "kind": "toolkit",
+      "released": "2021-10",
       "venue": "project page",
       "year": 2021,
       "status": "open",
     {
       "name": "Ego-Exo4D CLI and docs",
       "kind": "toolkit",
+      "released": "2023-11",
       "venue": "project page",
       "year": 2023,
       "status": "open",
     {
       "name": "VISOR API",
       "kind": "toolkit",
+      "released": "2022-09",
       "venue": "GitHub",
       "year": 2022,
       "status": "open",
     {
       "name": "EgoObjects API",
       "kind": "toolkit",
+      "released": "2023-09",
+      "venue": "arXiv",
       "year": 2023,
       "status": "open",
       "scope": "egocentric",
       "url": "https://github.com/facebookresearch/EgoObjects",
+      "paper": "https://arxiv.org/abs/2309.08816",
       "scale": "Working with category and instance-level egocentric object labels.",
       "tasks": [
         "tooling"
     {
       "name": "HOT3D tooling",
       "kind": "toolkit",
+      "released": "2024-06",
       "venue": "project page",
       "status": "open",
       "scope": "egocentric",
     {
       "name": "HOI4D tooling",
       "kind": "toolkit",
+      "released": "2022-03",
       "venue": "project page",
       "year": 2022,
       "status": "open",
     {
       "name": "EgoMemory",
       "kind": "benchmark",
+      "released": "2025-09",
       "venue": "OpenReview",
+      "year": 2025,
       "status": "watch",
       "scope": "egocentric",
       "url": "https://openreview.net/forum?id=T0em4hJCQb",
     {
       "name": "EgoTextVQA",
       "kind": "benchmark",
+      "released": "2025-06",
       "venue": "CVPR 2025",
       "year": 2025,
       "status": "open",
     {
       "name": "EgoVQA",
       "kind": "benchmark",
+      "released": "2019-10",
       "venue": "ICCV 2019",
       "year": 2019,
       "status": "open",
     {
       "name": "EgoSurgery",
       "kind": "dataset",
+      "released": "2025-03",
+      "venue": "arXiv",
+      "year": 2025,
       "status": "open",
       "scope": "egocentric",
       "url": "https://github.com/Fujiry0/EgoSurgery",
     {
       "name": "CMU-MMAC",
       "kind": "dataset",
+      "released": "2009-06",
       "venue": "CMU tech report 2009",
       "year": 2009,
       "status": "open",
     {
       "name": "First-Person Social Interactions",
       "kind": "dataset",
+      "released": "2012-06",
       "venue": "CVPR 2012",
       "year": 2012,
       "status": "open",
     {
       "name": "BEOID",
       "kind": "dataset",
+      "released": "2014-09",
       "venue": "BMVC 2014",
       "year": 2014,
       "status": "open",
     {
       "name": "First-Person Pose Recognition",
       "kind": "model",
+      "released": "2015-06",
       "venue": "CVPR 2015",
       "year": 2015,
       "status": "watch",

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