Bridging the Embodiment Gap: Disentangled Cross-Embodiment Video Editing

• URL: http://arxiv.org/abs/2605.03637v1
• Date: Tue, 05 May 2026 11:09:41 GMT
• Title: Bridging the Embodiment Gap: Disentangled Cross-Embodiment Video Editing
• Abstract summary: We propose a generative framework for cross-embodiment video editing.<n>Our method factorizes a demonstration video into two latent spaces by enforcing a dual contrastive objective.<n>A parameter-efficient adapter injects these latent codes into a frozen video diffusion model, enabling the synthesis of a coherent robot execution video.
• Score: 35.3765036136913
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning robotic manipulation from human videos is a promising solution to the data bottleneck in robotics, but the distribution shift between humans and robots remains a critical challenge. Existing approaches often produce entangled representations, where task-relevant information is coupled with human-specific kinematics, limiting their adaptability. We propose a generative framework for cross-embodiment video editing that directly addresses this by learning explicitly disentangled task and embodiment representations. Our method factorizes a demonstration video into two orthogonal latent spaces by enforcing a dual contrastive objective: it minimizes mutual information between the spaces to ensure independence while maximizing intra-space consistency to create stable representations. A parameter-efficient adapter injects these latent codes into a frozen video diffusion model, enabling the synthesis of a coherent robot execution video from a single human demonstration, without requiring paired cross-embodiment data. Experiments show our approach generates temporally consistent and morphologically accurate robot demonstrations, offering a scalable solution to leverage internet-scale human video for robot learning.

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