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ReLift reframes dense 3D tracking from monocular video as geometric trajectory repair, using a feed-forward trajectory corrector that alternates attention along and across lifted trajectories. It achieves state-of-the-art mean accuracy on WorldTrack, improving over the strongest baselines by 2.1 APD on all points and 3.0 on dynamic points without image-based training.
This paper analyzes failures in reconstruction-based unsupervised learning through geometric conditions and proposes new methods like Dynamic Push and Pull to enhance anomaly detection performance.