How to use from the
Use from the
Diffusers library
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import load_image, export_to_video

# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("HorizonRobotics/RoboTransfer", dtype=torch.bfloat16, device_map="cuda")
pipe.to("cuda")

prompt = "A man with short gray hair plays a red electric guitar."
image = load_image(
    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png"
)

output = pipe(image=image, prompt=prompt).frames[0]
export_to_video(output, "output.mp4")

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

Liu Liu, Xiaofeng Wang, Guosheng Zhao, Keyu Li, Wenkang Qin, Jiaxiong Qiu, Zheng Zhu, Guan Huang, Zhizhong Su
RoboTransfer

πŸ” Abstract

RoboTransfer Pipeline

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

  • πŸ“ 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.
  • πŸ” Cross-View Conditioning: Learns representations from multiple camera views with spatial correspondence.
  • πŸ€– Robotic Policy Transfer: Facilitates domain adaptation by generating synthetic training data in target domains.

πŸ“– 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}
}
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