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arXiv · 2306.08637

TAPIR: Tracking Any Point with per-frame Initialization and temporal Refinement

Carl Doersch·Yi Yang·Mel Vecerik·Dilara Gokay·Ankush Gupta·Yusuf Aytar·Joao Carreira·Andrew Zisserman
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Abstract

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.

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Carl Doersch, Yi Yang, Mel Vecerik, Dilara Gokay, Ankush Gupta, Yusuf Aytar, Joao Carreira, Andrew Zisserman. 2023-06-14. TAPIR: Tracking Any Point with per-frame Initialization and temporal Refinement. https://arxiv.org/abs/2306.08637

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