Hand Kinematics as an Implicit Physical Sensor for Egocentric Grasp Pressure Estimation

Estimating grasp pressure from egocentric video is a partially observed and underdetermined inverse contact problem, as deformation at the contact interface is often occluded. Existing methods rely mainly on RGB appearance and overlook hand motion as a source of physical evidence. We present KIPS, which treats hand kinematics as an implicit physical sensor. During stable grasp transport, hand and object motion are mechanically coupled, making the recovered hand trajectory informative about the contact load required to support and accelerate the object. KIPS represents motion through displacement, velocity, and acceleration at multiple granularities, then learns an implicit mapping from these observations to dense sensor responses. RGB features optionally condition the model with complementary appearance cues. Across EgoTactile and OpenTouch, KIPS without direct RGB appearance conditioning outperforms appearance-based baselines on most metrics, while adding RGB features yields further improvements. Motion-stratified analysis shows that stronger motion coincides with larger relative pressure changes and smaller gains from RGB, underscoring the value of kinematic evidence in dynamic interactions.