EgoPHI: 一人称視点映像からの接触と力の推定
EgoPHI: Estimating Contact and Force from Egocentric Vision
単眼RGB画像と物体形状から、手と物体のメッシュ上の密な接触マップと3次元力分布を同時推定する手法を提案。物理シミュレーションで教師データを生成し、実世界データでも有効性を検証した。
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著者: Andela Ilic, Rachel Schuchert, Yijing Jiang, Christian Holz
分類: cs.CV, cs.GR, cs.HC, cs.RO
原文アブストラクト
Understanding hand-object interaction from egocentric vision is essential for modeling how people physically engage with the surrounding world. Yet reasoning about physically grounded interaction requires estimating the forces acting on hands and objects, beyond localizing contact. We present EgoPHI, the first method that jointly estimates dense contact maps and 3D force distributions on hand and object meshes from a single monocular RGB image and object geometry. To address the lack of scalable ground-truth force annotations, we introduce a physics-based simulation pipeline that augments existing hand-object datasets with dense per-vertex force supervision. EgoPHI then learns dense 3D contact and force on interacting hand and articulated object meshes, extending vision-based force estimation beyond image-space or planar settings. Our evaluation on in-distribution and out-of-distribution benchmarks shows that EgoPHI improves force estimation over existing approaches while generalizing to unseen datasets. To evaluate sim-to-real transfer, we constructed two physical objects that capture dense object contact and force magnitude and used them to record a dataset of interactions from eight participants across diverse touch and grasp types. Our results demonstrate that EgoPHI recovers meaningful 3D contact and force distributions in simulated, out-of-distribution, and real-world settings, advancing egocentric hand-object understanding from contact localization toward physically grounded interaction reasoning.
