EgoPhys: 一人称視点動画から変形物体の汎用的物理モデルを学習する
EgoPhys: Learning Generalizable Physics Models of Deformable Objects from Egocentric Video
一人称視点のRGB動画のみから変形物体の物理デジタルツインを構築するフレームワークを提案し、未知物体へのゼロショット一般化を実現した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
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著者: Hyunjin Kim, Ri-Zhao Qiu, Guangqi Jiang, Xiaolong Wang
分類: cs.CV, cs.AI, cs.RO
原文アブストラクト
Humans naturally understand object physics through everyday interactions, but faithfully predicting complex deformable dynamics, such as elastic materials and fabrics, remains a major challenge for computer vision and robotics. We present EgoPhys, a framework that constructs deformable physical digital twins from egocentric RGB-only video using generalizable priors. EgoPhys overcomes the limitations of existing methods to enable controllable deformable digital twin generation from egocentric videos by distilling per-object inverse-physics solutions into a compact codebook, enabling prediction of dense spring stiffness fields for unseen objects without per-spring test-time optimization. Trained with generalizable priors from diverse egocentric interactions, EgoPhys outperforms baselines in reconstruction, future prediction, and zero-shot generalization. To support training and evaluation, we curate an egocentric interaction dataset covering diverse deformable objects, scenes, and manipulation styles. We deploy EgoPhys on a real xArm6 robot, demonstrating that a digital twin initialized from a single egocentric human play video can serve as an internal world representation to aid in deformable-object planning, highlighting egocentric RGB observations as a scalable path toward real-to-sim pipelines.
