Masquerade: Learning from In-the-wild Human Videos using Data-Editing
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| Format: | Preprint |
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2025
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| _version_ | 1866914610300321792 |
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| author | Lepert, Marion Fang, Jiaying Bohg, Jeannette |
| author_facet | Lepert, Marion Fang, Jiaying Bohg, Jeannette |
| contents | Robot manipulation research still suffers from significant data scarcity: even the largest robot datasets are orders of magnitude smaller and less diverse than those that fueled recent breakthroughs in language and vision. We introduce Masquerade, a method that edits in-the-wild egocentric human videos to bridge the visual embodiment gap between humans and robots and then learns a robot policy with these edited videos. Our pipeline turns each human video into robotized demonstrations by (i) estimating 3-D hand poses, (ii) inpainting the human arms, and (iii) overlaying a rendered bimanual robot that tracks the recovered end-effector trajectories. Pre-training a visual encoder to predict future 2-D robot keypoints on 675K frames of these edited clips, and continuing that auxiliary loss while fine-tuning a diffusion policy head on only 50 robot demonstrations per task, yields policies that generalize significantly better than prior work. On three long-horizon, bimanual kitchen tasks evaluated in three unseen scenes each, Masquerade outperforms baselines by 5-6x. Ablations show that both the robot overlay and co-training are indispensable, and performance scales logarithmically with the amount of edited human video. These results demonstrate that explicitly closing the visual embodiment gap unlocks a vast, readily available source of data from human videos that can be used to improve robot policies. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_09976 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Masquerade: Learning from In-the-wild Human Videos using Data-Editing Lepert, Marion Fang, Jiaying Bohg, Jeannette Robotics Robot manipulation research still suffers from significant data scarcity: even the largest robot datasets are orders of magnitude smaller and less diverse than those that fueled recent breakthroughs in language and vision. We introduce Masquerade, a method that edits in-the-wild egocentric human videos to bridge the visual embodiment gap between humans and robots and then learns a robot policy with these edited videos. Our pipeline turns each human video into robotized demonstrations by (i) estimating 3-D hand poses, (ii) inpainting the human arms, and (iii) overlaying a rendered bimanual robot that tracks the recovered end-effector trajectories. Pre-training a visual encoder to predict future 2-D robot keypoints on 675K frames of these edited clips, and continuing that auxiliary loss while fine-tuning a diffusion policy head on only 50 robot demonstrations per task, yields policies that generalize significantly better than prior work. On three long-horizon, bimanual kitchen tasks evaluated in three unseen scenes each, Masquerade outperforms baselines by 5-6x. Ablations show that both the robot overlay and co-training are indispensable, and performance scales logarithmically with the amount of edited human video. These results demonstrate that explicitly closing the visual embodiment gap unlocks a vast, readily available source of data from human videos that can be used to improve robot policies. |
| title | Masquerade: Learning from In-the-wild Human Videos using Data-Editing |
| topic | Robotics |
| url | https://arxiv.org/abs/2508.09976 |