TAPNext: Tracking Any Point (TAP) as Next Token Prediction
This addresses a challenging computer vision problem with applications in robotics, video editing, and 3D reconstruction, representing a novel method rather than an incremental improvement.
The paper tackles the problem of Tracking Any Point (TAP) in videos by proposing TAPNext, which casts it as sequential masked token decoding, achieving new state-of-the-art performance among online and offline trackers.
Tracking Any Point (TAP) in a video is a challenging computer vision problem with many demonstrated applications in robotics, video editing, and 3D reconstruction. Existing methods for TAP rely heavily on complex tracking-specific inductive biases and heuristics, limiting their generality and potential for scaling. To address these challenges, we present TAPNext, a new approach that casts TAP as sequential masked token decoding. Our model is causal, tracks in a purely online fashion, and removes tracking-specific inductive biases. This enables TAPNext to run with minimal latency, and removes the temporal windowing required by many existing state of art trackers. Despite its simplicity, TAPNext achieves a new state-of-the-art tracking performance among both online and offline trackers. Finally, we present evidence that many widely used tracking heuristics emerge naturally in TAPNext through end-to-end training. The TAPNext model and code can be found at https://tap-next.github.io/.