README.md

---
library_name: diffusers
license: apache-2.0
pipeline_tag: image-to-video
---

<h1 align="center">
  RoboTransfer: Geometry-Consistent Video Diffusion for Robotic Visual Policy Transfer
</h1>


<div align="center" class="authors">
  Liu Liu,
  Xiaofeng Wang,
  Guosheng Zhao,
  Keyu Li,
  Wenkang Qin,
  Jiaxiong Qiu,
  Zheng Zhu,
  Guan Huang,
  Zhizhong Su
</div>

<div align="center" style="line-height: 3;">
  <a href="https://github.com/HorizonRobotics/RoboTransfer" target="_blank" style="margin: 2px;">
    <img alt="Code" src="https://img.shields.io/badge/Code-Github-blue" style="display: inline-block; vertical-align: middle;"/>
  </a>
  <a href="https://horizonrobotics.github.io/robot_lab/robotransfer" target="_blank" style="margin: 2px;">
    <img alt="Project Page" src="https://img.shields.io/badge/🌐-Project_Page-blue" style="display: inline-block; vertical-align: middle;"/>
  </a>
  <a href="https://arxiv.org/abs/2505.23171" target="_blank" style="margin: 2px;">
    <img alt="arXiv" src="https://img.shields.io/badge/📄-arXiv-b31b1b" style="display: inline-block; vertical-align: middle;"/>
  </a>
  <a href="https://youtu.be/dGXKtqDnm5Q" target="_blank" style="margin: 2px;">
    <img alt="Video" src="https://img.shields.io/badge/🎥-Video-red" style="display: inline-block; vertical-align: middle;"/>
  </a>
  <a href="https://mp.weixin.qq.com/s/c9-1HPBMHIy4oEwyKnsT7Q" target="_blank" style="margin: 2px;">
    <img alt="中文介绍" src="https://img.shields.io/badge/中文介绍-07C160?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
  </a>
</div>

<div align="center">
  <img src="assets/pin.jpg" width="40%" alt="RoboTransfer"/></div>

---

## 🔍 Abstract

![RoboTransfer Pipeline](assets/robotransfer.jpg)

**RoboTransfer** is a novel diffusion-based video generation framework tailored for robotic visual policy transfer. Unlike conventional approaches, RoboTransfer introduces **geometry-aware synthesis** by injecting **depth and normal priors**, ensuring multi-view consistency across dynamic robotic scenes. The method further supports **explicit control over scene components**, such as **background editing**, **object identity swapping**, and **motion specification**, offering a fine-grained video generation pipeline that benefits embodied learning.

---

## 🧠 Key Features

- 📐 **Geometry-Consistent Diffusion**: Injects global 3D cues (depth, normal) and cross-view interactions for multi-view realism.
- 🧩 **Scene Component Control**: Enables manipulation of object attributes (pose, identity) and background features.
- 🔁 **Cross-View Conditioning**: Learns representations from multiple camera views with spatial correspondence.
- 🤖 **Robotic Policy Transfer**: Facilitates domain adaptation by generating synthetic training data in target domains.

---

## 📖 BibTeX

```bibtex
@article{liu2025robotransfer,
  title={RoboTransfer: Geometry-Consistent Video Diffusion for Robotic Visual Policy Transfer},
  author={Liu, Liu and Wang, Xiaofeng and Zhao, Guosheng and Li, Keyu Li, Wenkang Qin, Jiaxiong Qiu, Zheng Zhu, Guan Huang, Zhizhong Su},
  journal={arXiv preprint arXiv:2505.23171},
  year={2025}
}
```