Tracking Everything Everywhere All at Once
Authors: name: Qianqian Wang, name: Yen-Yu Chang, name: Ruojin Cai, name: Zhengqi Li, name: Bharath Hariharan, name: Aleksander Holynski, name: Noah Snavely

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