MEgoVista: 実環境におけるメートル単位の4D手・頭部動作推定のための多視点自己中心運動推定
MEgoVista: Multi-view Ego-aware Motion Estimation for Metric 4D Hands and Head in the Wild
未準備の自己中心視点録画から、キャリブレーション済みステレオを用いてメートル単位の両手と頭部の動作を重力整合した世界座標系で再構成するオフラインパイプラインを提案。
詳しい要約
1. どんなもの?
2. 先行研究と比べてどこがすごい?
3. 技術・手法の肝は?
4. どうやって有効だと検証した?
5. 議論はある?
6. 次に読むべき論文は?
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Jiangong Xiao, Zhihao Zhang, Yifei Dong, Chao Ma, Zhouyi Jin, Zhiwen Hou, Li Liu, Weihuang Chen, Hongbin Sun, Maoqing Yao
分類: cs.CV
原文アブストラクト
Learning manipulation from human video requires high-fidelity hand-motion reconstruction in metric units. Today's metric hand labels come from studio rigs and instrumented headsets, and both are confined in the same two ways: neither leaves a prepared setting, and neither is checked against an independent reference. Unconstrained head-worn recording promises the opposite trade-off, scaling with the number of people wearing a device. We therefore introduce MEgoVista, an offline pipeline that turns a single unprepared MEgo View recording into metric two-hand and head motion in one gravity-aligned world frame. Three properties set it apart from existing egocentric reconstruction systems: first, it reconstructs in settings studio volumes and tabletop rigs cannot reach, settling hand ownership at detection so bystander hands stay out of the wearer's trajectory; second, it takes its metric gauge from calibrated stereo rather than a monocular prior, installing scale at initialisation so policies receive physical units, not arbitrary coordinates; third, both outputs are scored inside a motion-capture volume against independent Chingmu optical capture, under a protocol that audits its own reference and charges what a method declines to predict. MEgoVista is offered as a measured route from egocentric video to metric hand supervision, one that widens where such labels can be gathered.