HOPE: Hand-Object Pressure Estimation from Monocular Videos
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TL;DR - HOPE is a monocular-video method that predicts per-vertex normal pressure and contact directly on a hand mesh, reframing vision-based pressure estimation as hand-centric video prediction instead of planar, single-image inference. It matters because contact-rich hand-object interaction understanding (robotics, AR/VR, manipulation learning) needs pressure signals without instrumented surfaces or gloves at test time.
- Output space is the hand mesh itself, making predictions independent of object shape and sensor layout, and enabling dynamic multi-object scenarios that planar-surface baselines cannot handle.
- Heterogeneous supervision is unified by lifting tactile-glove pressure, planar-sensor pressure, and distance-based contact annotations into a shared hand vertex space, so bare-hand contact data regularizes learning where metric pressure labels are missing.
- Architecture is a vertex-anchored video transformer treating each vertex as a persistent token that aggregates visual features and hand pose over time, with a contact-gated pressure head enforcing zero pressure without contact.
- Evaluated on OpenTouch, PressureVisionDB, and hand-object contact benchmarks across object-pressure, surface-pressure, and contact-supervised HOI settings; the authors report generalization from gloved-hand training video to bare-hand egocentric and in-the-wild video.
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HOPE: Hand-Object Pressure Estimation from Monocular Videos
TL;DR - HOPE is a monocular-video method that predicts per-vertex normal pressure and contact directly on a hand mesh, reframing vision-based pressure estimation as hand-centric video prediction instead of planar, single-image inference. It matters because contact-rich hand-object interaction understanding (robotics, AR/VR, manipulation learning) needs pressure signals without instrumented surfaces or gloves at test time.
- Output space is the hand mesh itself, making predictions independent of object shape and sensor layout, and enabling dynamic multi-object scenarios that planar-surface baselines cannot handle.
- Heterogeneous supervision is unified by lifting tactile-glove pressure, planar-sensor pressure, and distance-based contact annotations into a shared hand vertex space, so bare-hand contact data regularizes learning where metric pressure labels are missing.
- Architecture is a vertex-anchored video transformer treating each vertex as a persistent token that aggregates visual features and hand pose over time, with a contact-gated pressure head enforcing zero pressure without contact.
- Evaluated on OpenTouch, PressureVisionDB, and hand-object contact benchmarks across object-pressure, surface-pressure, and contact-supervised HOI settings; the authors report generalization from gloved-hand training video to bare-hand egocentric and in-the-wild video.