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google-deepmind/tapnet ★ 1981 188 DATA_RESEARCH

This repository from Google DeepMind implements a family of point tracking models (TAP-Net, TAPIR, BootsTAP, TAPNext, TAPNext++, TRAJAN) and the associated TAP-Vid and TAPVid-3D benchmarks. It is primarily a research codebase for tracking arbitrary points in video, with both JAX/Haiku and PyTorch implementations. The code includes training pipelines (Jaxline), evaluation scripts, data generation for synthetic (Kubric) and real datasets (DAVIS, Kinetics, ADT, DriveTrack, Panoptic Studio), and interactive Colab demos. While it has application-like components such as live webcam demos and a VOT challenge tracker, the dominant character is research: the code is organized around benchmarking and model experimentation rather than production deployment. The repository is actively maintained with recent commits and provides extensive documentation for reproducing results.

RATING: 8.7/10 REPRO: 7.0/10 MAINT: active DRIFT: low
#point-tracking #video-understanding #deep-learning #computer-vision #research #tapnet #tapir #tapnext #benchmark #robotics
▸ 4 reusable components found
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