Duplicate from datapointai/text-2-video-human-preferences-motion

Co-authored-by: Datapoint AI <[email protected]>

.gitattributes

@@ -0,0 +1,60 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.avro filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,234 @@
+---
+pretty_name: "Human Preference Data for AI Video Generation — Motion Quality (29K Labels, 4 Models)"
+language:
+  - en
+license: cc-by-4.0
+size_categories:
+  - 10K<n<100K
+task_categories:
+  - video-classification
+  - text-to-video
+  - reinforcement-learning
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: data/train-*.parquet
+tags:
+  - human-preferences
+  - video-generation
+  - preference-data
+  - human-motion
+  - rlhf
+  - reward-model
+  - text-to-video
+  - video-quality
+  - pairwise-comparison
+  - annotation
+  - video-evaluation
+  - video-benchmark
+  - dpo
+  - human-feedback
+  - ai-video
+  - generative-ai
+  - sora
+  - veo
+  - kling
+  - grok
+  - luma
+  - coherence
+  - aesthetics
+  - prompt-adherence
+  - motion-quality
+  - temporal-consistency
+  - video-reward-model
+  - preference-learning
+  - video-rlhf
+---
+
+# Human Preferences for AI-Generated Video: Motion Quality
+
+<p align="left">
+  <img src="https://huggingface.co/datasets/datapointai/text-2-video-human-preferences-motion/resolve/main/datapointlogo.png" alt="Datapoint AI" width="300">
+</p>
+
+**29,283 pairwise human preference labels** comparing **4 frontier video generation models** on human motion across **3 quality dimensions**, collected from **4,349 real annotators** via [Datapoint AI](https://trydatapoint.com).
+
+This is the largest publicly available human preference dataset focused specifically on **human motion in AI-generated video**.
+
+## Why This Dataset
+
+Video generation models are improving fast, but **evaluating human motion remains unsolved**. Automated judges (VLMs like GPT-4V, Gemini) miss subtle errors in gait, facial expressions, and multi-body coordination that humans catch easily.
+
+This dataset gives you **ground-truth human preferences** you can use to:
+
+- **Train video reward models** for RLHF / DPO / preference optimization
+- **Benchmark video generation models** on realistic human motion
+- **Calibrate VLM judges** — measure where automated evaluators disagree with humans
+- **Study annotation patterns** — inter-annotator agreement, position bias, response time distributions
+
+## Models Compared
+
+| Model | Type |
+|---|---|
+| **Grok Imagine** | xAI's video generation model |
+| **Veo 3 Fast** | Google DeepMind |
+| **Kling 1.5 Pro** | Kuaishou |
+| **Luma Ray 2** | Luma Labs |
+
+## Dataset Structure
+
+354 aggregated comparison rows (from 29,283 individual annotations). Each row = one pairwise comparison between two model outputs for the same prompt.
+
+| Field | Description |
+|---|---|
+| `prompt` | Text prompt used to generate both videos |
+| `video1` / `video2` | GIF previews of the generated videos |
+| `model1` / `model2` | Which model generated each video |
+| `weighted_results1_Coherence` | Fraction of annotators preferring video 1 on coherence |
+| `weighted_results2_Coherence` | Fraction preferring video 2 on coherence |
+| `weighted_results1_Aesthetic` | Fraction preferring video 1 on aesthetics |
+| `weighted_results2_Aesthetic` | Fraction preferring video 2 on aesthetics |
+| `weighted_results1_Prompt_Adherence` | Fraction preferring video 1 on prompt faithfulness |
+| `weighted_results2_Prompt_Adherence` | Fraction preferring video 2 on prompt faithfulness |
+| `detailedResults_*` | Per-annotator votes with timestamps |
+| `subcategory` | Motion type: walking, dancing, talking, sports, stationary, multi-person |
+| `prompt_id` | Unique prompt identifier (1–60) |
+
+## Evaluation Dimensions
+
+| Dimension | What annotators judged |
+|---|---|
+| **Coherence** | Temporal consistency — no flickering, warping, deformation, or physically implausible motion |
+| **Aesthetic** | Visual quality — composition, lighting, color, style, production value |
+| **Prompt Adherence** | Accuracy — does the video depict what the prompt describes? |
+
+## Motion Categories
+
+| Category | Examples | Why it's hard for AI |
+|---|---|---|
+| **Walking / Running** | Gaits, jogging, sprinting | Weight shift, foot contact, natural rhythm |
+| **Dancing** | Ballet, hip-hop, folk | Complex coordinated movement, full-body flow |
+| **Talking / Expressions** | Speaking, singing, laughing | Lip sync, facial micro-movements |
+| **Sports / Action** | Martial arts, skateboarding | Fast motion, physics, athletic poses |
+| **Stationary** | Meditating, reading, posing | Subtle motion, identity preservation over time |
+| **Multi-Person** | Handshakes, sparring, group performance | Two+ bodies, occlusion, interaction physics |
+
+## Key Results
+
+### Overall Win Rates
+
+| Rank | Model | Win Rate | 95% CI |
+|---|---|---|---|
+| 1 | **Grok Imagine** | 54.7% | [54.0%, 55.5%] |
+| 2 | **Veo 3 Fast** | 54.6% | [53.8%, 55.3%] |
+| 3 | **Kling 1.5 Pro** | 47.9% | [47.1%, 48.7%] |
+| 4 | **Luma Ray 2** | 42.8% | [42.0%, 43.6%] |
+
+### By Dimension
+
+| Model | Coherence | Aesthetic | Prompt Adherence |
+|---|---|---|---|
+| Grok Imagine | 53.6% | **55.7%** | 54.7% |
+| Veo 3 Fast | 54.5% | 54.7% | 54.5% |
+| Kling 1.5 Pro | 48.4% | 48.0% | 47.4% |
+| Luma Ray 2 | 43.5% | 41.5% | 43.5% |
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("datapointai/text-2-video-human-preferences-motion")
+print(ds["train"][0])
+```
+
+### Train a reward model
+
+```python
+import pandas as pd
+from datasets import load_dataset
+
+ds = load_dataset("datapointai/text-2-video-human-preferences-motion", split="train")
+df = ds.to_pandas()
+
+# Each row is a comparison — use weighted scores as soft labels
+for _, row in df.iterrows():
+    prompt = row["prompt"]
+    score_a_coherence = row["weighted_results1_Coherence"]
+    score_b_coherence = row["weighted_results2_Coherence"]
+    # Use as preference pairs for DPO, reward modeling, etc.
+```
+
+## Data Quality
+
+| Metric | Value |
+|---|---|
+| Total annotations | 29,283 |
+| Unique annotators | 4,349 |
+| Unique prompts | 60 |
+| Pairwise comparisons | 354 |
+| Annotations per comparison | ~28 (median) |
+| Median response time | 14.9 seconds |
+| Position bias | 52.8% left / 47.2% right (near 50/50) |
+
+**Position bias control**: Videos were randomly shuffled between left/right for each comparison. Observed selection rate is near the 50/50 baseline.
+
+**Engagement verification**: Median 14.9s response time confirms annotators watched both videos (each 4–5 seconds) before deciding.
+
+**Annotator diversity**: 4,349 unique annotators with a median of 4 labels each — broad perspectives, low individual bias.
+
+## Methodology
+
+- **60 prompts** generated with structured diversity across motion categories
+- **4 models** evaluated via Fal.ai API (single inference, no cherry-picking)
+- **All videos** are 4–5 seconds, 540p–720p, 16:9
+- **Mobile-first annotation** through Datapoint AI's consumer app SDK
+- **Forced-choice** pairwise comparison with dimension-specific questions
+- **Dawid-Skene aggregation** available for consensus estimation
+
+## Compared to Other Datasets
+
+| Dataset | Labels | Focus | Models | Dimensions |
+|---|---|---|---|---|
+| **This dataset** | **29,283** | **Human motion** | **4 frontier (2025)** | **3** |
+| Rapidata text-2-video | 2,570 | General video | 4 | 3 |
+| VideoGen-Eval | ~5,000 | General video | 6 | 1 |
+
+## Get Custom Human Preference Data
+
+Need preference labels for **your** model, domain, or evaluation criteria?
+
+Datapoint AI runs the same annotation pipeline used to create this dataset — but customized to your specs:
+
+- **Your models** — any video, image, or text generation model
+- **Your prompts** — domain-specific evaluation sets
+- **Your dimensions** — custom quality criteria beyond coherence/aesthetics/adherence
+- **Scale** — from 1K to 1M+ labels, median 24-hour turnaround
+- **No professional annotator bias** — real users in a consumer app, not Mechanical Turk
+
+🎓 **First dataset free for university researchers and early-stage startups.**
+
+👉 **[Get started at trydatapoint.com](https://trydatapoint.com)** or email **[email protected]**
+
+## Citation
+
+```bibtex
+@dataset{datapointai_vidprefmotion_2026,
+  title={Human Preference Data for AI Video Generation: Motion Quality},
+  author={Datapoint AI},
+  year={2026},
+  url={https://huggingface.co/datasets/datapointai/text-2-video-human-preferences-motion},
+  note={29,283 pairwise human preference labels for AI-generated human motion video}
+}
+```
+
+## License
+
+CC-BY-4.0 — free for research and commercial use with attribution.
+
+## About Datapoint AI
+
+[Datapoint AI](https://trydatapoint.com) collects human preference data at scale through a mobile-first annotation pipeline embedded in consumer apps. We replace mobile ads with data labeling tasks — real users, real preferences, no professional annotator bias.
+
+For custom evaluation studies, higher-scale labeling, or API access: **[trydatapoint.com](https://trydatapoint.com)**

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f6b54e9276b5f74700307aec96def0bb10220156a76793c016a1d54141d19e1b
+size 844148