Ruicheng Li, Qixiu Li, Yu Deng, Sicheng Xu, Jiaolong Yang, Baining Guo · 2026 · arXiv
DOI: 10.48550/arXiv.2610.26780 · open-access PDF
Egocentric 비디오는 로봇 학습, 증폭 현실 및 인간 활동 이해를 포함한 다양한 응용 프로그램을 지원합니다. 그러한 비디오에서 메트릭 3D 핸드 트랙토리를 복구하는 것은 손의 움직임과 카메라의 움직임을 측정하여 손을 일관된 참조 프레임으로 표현해야합니다. 많은 기존 파이프라인은 손의 움직임과 카메라의 움직임을 별도의 단계로 복구하여 손 감지, 손 재건, 카메라 추적 및 트랙토리 정화를 결합합니다.
Machine translation (Meta M2M100); the English original follows.
Egocentric video supports diverse applications, including robot learning, augmented reality, and human activity understanding. Recovering metric 3D hand trajectories from such video requires estimating both hand motion and camera egomotion to express the hands in a consistent reference frame. Many existing pipelines recover hand and camera motion in separate stages, combining hand detection, hand reconstruction, camera tracking, and trajectory refinement. We present V-JEPA Hand, a simple feed-forward video model that jointly reconstructs bilateral hand motion and camera motion from egocentric
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