We present a novel model for Tracking Any Point (TAP) that effectively tracks any queried point on any physical surface throughout a video sequence. Our approach employs two stages: (1) a matching stage, which independently locates a suitable candidate point match for the query point on every other frame, and (2) a refinement stage, which updates both the trajectory and query features based on local correlations. The resulting model surpasses all baseline methods by a significant margin on the TAP-Vid benchmark, as demonstrated by an approximate 20% absolute average Jaccard (AJ) improvement on DAVIS. Our model facilitates fast inference on long and high-resolution video sequences. On a modern GPU, our implementation has the capacity to track points faster than real-time, and can be flexibly extended to higher-resolution videos. Given the high-quality trajectories extracted from a large dataset, we demonstrate a proof-of-concept diffusion model which generates trajectories from static images, enabling plausible animations. Visualizations, source code, and pretrained models can be found on our project webpage.
@article{arxiv.2306.08637,
title = {TAPIR: Tracking Any Point with per-frame Initialization and temporal Refinement},
author = {Carl Doersch and Yi Yang and Mel Vecerik and Dilara Gokay and Ankush Gupta and Yusuf Aytar and Joao Carreira and Andrew Zisserman},
journal= {arXiv preprint arXiv:2306.08637},
year = {2023}
}