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

Code

• github.com/deepmind/tapnet
  OfficialIn paperjax★ 1,820
• github.com/riponazad/echotracker
  pytorch★ 56
• github.com/ibaiGorordo/Tapir-Pytorch-Inference
  pytorch★ 18

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.

Tasks

Benchmark Results

Reproductions