3D Hand Pose Estimation for Robotics

Hand pose estimation recovers the 3D positions of hand and finger joints across a clip, usually as a per-frame skeleton in metric units. For manipulation, it is the difference between a model knowing a hand is present in frame and knowing exactly how that hand is shaped around an object at the moment of contact.

On its own, RGB video leaves a model to infer 3D hand structure it often cannot see reliably through occlusion, motion blur, and self-contact between fingers. 3D hand pose data replaces that inference with a measured joint skeleton, tracked separately for each hand.

Manipulation policies need the fine geometry of grasp and release: finger spread at approach, contact points at grip, and the release trajectory as an object is set down. A dataset that only labels object bounding boxes and a coarse hand mask cannot teach that geometry.

Two-handed and bimanual tasks raise the bar further. Per-hand pose with visibility flags lets a policy learn coordination and handoff between hands, including which hand is occluded or out of frame at any given instant, rather than collapsing both hands into one signal.

A skill spec that calls for 3D hand pose should name the tasks where grasp and contact geometry actually matter, whether one or both hands need tracking, and any occlusion-heavy moments (reaching into a container, working behind another object) that should be captured deliberately rather than avoided.

Bimanual tasks are the clearest case for per-hand pose: coordination and handoff between two hands cannot be learned from a single collapsed hand signal.