ReLift: Dense 3D Tracking as Lifted Trajectory Repair
READ THE ORIGINAL SOURCEOpen original sourcehuggingface.co · Opens in a new tabReLift is a feed-forward framework for dense 3D tracking from monocular video. It combines pretrained 2D trackers and 3D geometry models, then applies a trajectory corrector that alternates attention along and across trajectories to repair corrupted positions and complete occluded motion. The corrector is trained only on geometric inputs with simulated lifting errors, so it can be reused across tracker and geometry combinations without retraining. The authors report state-of-the-art mean accuracy on WorldTrack, improving on the strongest baselines by 2.1 APD on all points and 3.0 on dynamic points.
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- Primary sourceHugging Face Daily Papers
- Discovery linkHugging Face
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ReLift is a feed-forward framework for dense 3D tracking from monocular video. It combines pretrained 2D trackers and 3D geometry models, then applies a trajectory corrector that alternates attention along and across trajectories to repair corrupted positions and complete occluded motion. The corrector is trained only on geometric inputs with simulated lifting errors, so it can be reused across tracker and geometry combinations without retraining. The authors report state-of-the-art mean accuracy on WorldTrack, improving on the strongest baselines by 2.1 APD on all points and 3.0 on dynamic points.
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Key findings from the stored brief
- Lifted trajectories with occluded segments interpolated already rival dedicated 3D trackers using the same geometry.
- A trajectory corrector trained on simulated lifting errors improves all tested tracker–geometry combinations without retraining.
- On WorldTrack, ReLift reports 2.1 APD gain on all points and 3.0 APD on dynamic points over strongest baselines.
Source claims not independently checked; results are reported on WorldTrack and the abstract gives no full benchmark breakdown.
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Evidence & sources
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Why this may matter
Decoupling 3D trajectory refinement from images and specific trackers lets the corrector benefit from future improvements in either 2D trackers or geometry models, addressing the scarcity of 4D supervision.
Limits and open questions
Source claims not independently checked; results are reported on WorldTrack and the abstract gives no full benchmark breakdown.
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