Decoupling Action from Egocentric Observation for World Simulation
Egocentric action transfer aims to decouple the semantic action from egocentric observations and reproduce them in new visual contexts, enabling scalable world simulation, embodied policy learning, and robotic data generation. Existing approaches to transferring actions rely on condition-guided video generation, which converts the observation video into explicit geometric controls (e.g., hand poses or meshes) to drive synthesis. However, these methods introduce geometric estimation errors, making it difficult to preserve physically plausible interactions when transferred to new scenes. Alternatively, motion transfer methods directly extract motion patterns from the observation video, yet ...
| Authors | Yue Ma, Pengjie Song, Xinyu Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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