RoboTransfer
Collection
Geometry-Consistent Video Diffusion for Robotic Visual Policy TransferοΌhttps://horizonrobotics.github.io/robot_lab/robotransfer/ β’ 3 items β’ Updated β’ 1
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 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.
@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}
}