CoTracker3: Simpler and Better Point Tracking by Pseudo-Labelling Real Videos

Nikita Karaev, Yuri Makarov, Jianyuan Wang, Natalia Neverova, Andrea Vedaldi, Christian Rupprecht; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025, pp. 6013-6022

We introduce CoTracker3, a new state-of-the-art point tracker. With CoTracker3, we revisit the design of recent trackers, removing components and reducing the number of parameters while also improving performance. We also explore the interplay of synthetic and real data. Recent trackers are trained on synthetic videos due to the difficulty of collecting tracking annotations for real data. However, this can result in suboptimal performance due to the statistical gap between synthetic and real videos. We thus suggest using off-the-shelf trackers as teachers, annotating real videos with pseudo-labels. Compared to other recent attempts at using real data for learning trackers, this scheme is much simpler and achieves better results using 1,000 times less data. CoTracker3 is available in online (causal) and offline variants and is particularly robust to occlusions.