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GigaHands strict-v1 full quality audit

Coverage

audit coverage
converted episodes 12,783 / 12,783
structural/temporal label frames 3,227,699 / 3,227,699
RGB projection-detector frames 63,915 / 63,915
RGB policy every episode, up to 5 evenly spaced frames

Results

category episodes rate
structural fatal error 0 0.000%
cam0: neither hand visible for more than half the episode 1,353 10.584%
cam0: neither hand visible for the whole episode 399 3.121%
persistent MANO-vs-triangulation disagreement (>5 cm median) 6 0.047%
output wrist temporal jump (>25 cm/frame) 2 0.016%
hand points behind selected camera (>5% joints) 31 0.243%
RGB detector: automatic alignment suspect 18 0.141%
RGB suspect: manually confirmed mismatch 0 0.000%
RGB suspect: unresolved after review 0 0.000%
RGB decode incomplete 0 0.000%

Neither hand projects into cam0 for 427,709 / 3,227,699 label frames (13.251%). These are not necessarily corrupt GigaHands motions, but they provide little or no image evidence for camera-conditioned VITRA training.

The detector raised 18 automatic RGB-alignment suspects. Manual review cleared 18: in those clips the projected MANO hand follows the real hand, while the detector fires on a monitor, object, toy dog, or a partially cropped hand with the wrong handedness. There are 0 confirmed mismatches and 0 unresolved automatic suspects. The review decision file is rgb_alignment_manual_review.json, and the rendered evidence is in the rgb_*_review_*.jpg files.

There are 213 episodes with a large MANO/triangulation tail discrepancy. In 185 of them the triangulated reference has a >10 cm jump and is more than twice as discontinuous as the converted MANO output, so those are more consistent with a noisy independent reference than with a converted-label failure. The remaining 28 are not automatically attributable to either source and stay in the conservative exclusion/review list. Their maximum-disagreement image overlays are in tail_disagreement_review_*.jpg.

Conservative training recommendation

Excluding low-visibility cam0 episodes, persistent or unresolved geometry disagreements, output temporal jumps, detector-confirmed RGB mismatches, and decode failures removes 1,384 episodes (10.827%). The exact IDs are in recommended_exclude_episode_ids.json; all category measurements are in audit_categories.json.

This is deliberately conservative. In particular, the visibility filter measures usefulness for VITRA training, not corruption of the underlying multiview motion. A better recovery policy is to select another official camera with high projected-hand visibility when that video is available, and only drop an episode when no camera is suitable.