RoboTransfer: Geometry-Consistent Video Diffusion for Robotic Visual Policy Transfer

Imitation Learning has become a fundamental approach in robotic manipulation. However, collecting large-scale real-world robot demonstrations is prohibitively expensive. Simulators offer a cost-effective alternative, but the sim-to-real gap make it extremely challenging to scale. Therefore, we introduce RoboTransfer, a diffusion-based video generation framework for robotic data synthesis. Unlike previous methods, RoboTransfer integrates multi-view geometry with explicit control over scene components, such as background and object attributes. By incorporating cross-view feature interactions and global depth/normal conditions, RoboTransfer ensures geometry consistency across views. This framework allows fine-grained control, including background edits and object swaps. Experiments demonstrate that RoboTransfer is capable of generating multi-view videos with enhanced geometric consistency and visual fidelity. In addition, policies trained on the data generated by RoboTransfer achieve a 33.3% relative improvement in the success rate in the DIFF-OBJ setting and a substantial 251% relative improvement in the more challenging DIFF-ALL scenario. Explore more demos on our project page:this https URL

@article{liu2025_2505.23171,
  title={ RoboTransfer: Geometry-Consistent Video Diffusion for Robotic Visual Policy Transfer },
  author={ Liu Liu and Xiaofeng Wang and Guosheng Zhao and Keyu Li and Wenkang Qin and Jiaxiong Qiu and Zheng Zhu and Guan Huang and Zhizhong Su },
  journal={arXiv preprint arXiv:2505.23171},
  year={ 2025 }
}