TAPIR: Tracking Any Point with per-frame Initialization and temporal Refinement
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.
PDF Abstract ICCV 2023 PDF ICCV 2023 AbstractDatasets
Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
---|---|---|---|---|---|---|
Visual Tracking | DAVIS | TAPIR (MOVi-E) | Average Jaccard | 59.8 | # 2 | |
Visual Tracking | DAVIS | TAPIR (Panning MOVi-E) | Average Jaccard | 61.3 | # 1 | |
Visual Tracking | Kinetics | TAPIR (Panning MOVi-E) | Average Jaccard | 57.2 | # 1 | |
Visual Tracking | Kinetics | TAPIR (MOVi-E) | Average Jaccard | 57.1 | # 2 | |
Visual Tracking | Kubric | TAPIR (Panning MOVi-E) | Average Jaccard | 84.7 | # 1 | |
Visual Tracking | Kubric | TAPIR (MOVi-E) | Average Jaccard | 84.3 | # 2 | |
Visual Tracking | RGB-Stacking | TAPIR (Panning MOVi-E) | Average Jaccard | 62.7 | # 2 | |
Visual Tracking | RGB-Stacking | TAPIR (MOVi-E) | Average Jaccard | 66.2 | # 1 |