RoboTryOn: Synthesizing Dexterous Robot Demonstrations from Human Videos via Virtual Try-On

Seungyong Lee, Suhyeok Jang, Minyoung Kim, Jinwoo Shin

RoboTryOn: Synthesizing Dexterous Robot Demonstrations from Human Videos via Virtual Try-On: 4 upvotes on Hugging Face Daily Papers, #30 of 46 papers on 2026-11-04. Day-by-day upvote history.

Upvotes over time

It had 4 upvotes by its first day, 4 upvotes by the next day, 4 upvotes by day 3, 4 upvotes by day 7, and has 4 now. It ranked #97 of 130 papers in its week and #97 of 130 in its month.

Robot foundation models have shown remarkable performance in robot control, yet adapting them to a new task still relies heavily on teleoperated demonstrations. Egocentric human videos offer a scalable substitute, but overlaying rendered robots on human videos or retargeting human poses into robot actions leaves significant visual or action gaps for high-DoF robot embodiments. To address this limitation, we propose RoboTryOn, which treats human-to-robot translation as virtual try-on: try-on keeps the person and swaps the garment, RoboTryOn keeps the demonstrated motion and swaps the human for the robot. Specifically, RoboTryOn converts a human video into a video of the target robot specified by a reference image, while preserving the original task, objects, and background. To maintain visual consistency, we translate selected keyframes autoregressively and use them to guide video synthesis with motion conditioned on the human hand skeleton. Additionally, we use an inverse dynamics model (IDM) that incorporates depth features to infer action labels from synthesized videos for policy learning. Our experiments across multiple humanoid embodiments show that policies trained with our synthetic data improve the average success rate on novel tasks, including challenging bimanual manipulation, from 2% to 60%, without any real robot demonstration of those tasks. Moreover, training with our synthetic data matches the performance of policies trained solely on teleoperated demonstrations at approximately a third of the collection cost, demonstrating the cost efficiency of our approach.

Paper page on Hugging Face · arXiv

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