Anonymous Institution(s)
A large-scale hybrid real-synthetic dataset containing 7,000 hours of human manipulation data seeded from 70 hours of real demonstrations, organized across 21 fundamental manipulation types with physics-validated contact forces.
DexCanvas is a large-scale hybrid real-synthetic human manipulation dataset containing 7,000 hours of dexterous hand-object interactions seeded from 70 hours of real human demonstrations. Each entry combines synchronized multi-view RGB-D, high-precision mocap with MANO hand parameters, and per-frame contact points with physically consistent force profiles.
22-camera optical mocap system captures hand kinematics and object trajectories at 30 Hz
Reinforcement learning policies reproduce demonstrations in physics simulation to extract contact forces
Per-frame contact points, force vectors, and object wrenches measured from simulator
Force-conditioned policy, and contact-driven retargeting to DexHand embodiments
The taxonomy hierarchically organizes manipulation strategies into four main categories: Power (whole-hand grasps), Intermediate (transitional grasps), Precision (fingertip control), and In-hand Manipulation (dynamic reorientation).
