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We present a new test-time optimization method for estimating dense andlong-range motion from a video sequence. Prior optical flow or particle videotracking algorithms typically operate within limited temporal windows,struggling to track through occlusions and maintain global consistency ofestimated motion trajectories. We propose a complete and globally consistentmotion representation, dubbed OmniMotion, that allows for accurate, full-lengthmotion estimation of every pixel in a video. OmniMotion represents a videousing a quasi-3D canonical volume and performs pixel-wise tracking viabijections between local and canonical space. This representation allows us toensure global consistency, track through occlusions, and model any combinationof camera and object motion. Extensive evaluations on the TAP-Vid benchmark andreal-world footage show that our approach outperforms prior state-of-the-artmethods by a large margin both quantitatively and qualitatively. See ourproject page for more results: http://omnimotion.github.io/

Tracking Everything Everywhere All at Once