Motion QA
Is the motion trainable?
Geometry QA covers a single instant. Robot policies learn from motion, so the same sequence has to hold consistent depth, trajectories, and action boundaries across every frame. These are the signals we validate before motion data is labeled.
Why it gates labelingValidates metric scale: depth that drifts off scale poisons the policy.
Depth → metric geometry
Per-pixel depth becomes a metric, gravity-aligned point cloud: the same scaffold geometry QA scores. Depth that drifts off metric scale is caught here, not in the policy.
depth · metric scale
Point tracks → trajectory consistency
Following the same surface points across frames recovers each object's trajectory. Reprojection error tells us which tracks are reliable enough to supervise a policy.
tracks · reprojection error
Motion masks → action labels
Separating what moves from what stays still segments actions over time, so a sequence carries temporal labels, not a single frozen instant.
masks · temporal coverage
4D reconstruction + tracking · depth · point tracks · motion masks · metrics: validation in progress