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---
license: apache-2.0
tags:
- vision
- tracking
---
# TAPNet
This repository contains the checkpoints of several point tracking models developed by DeepMind for point tracking.
🔗 **Code**: [https://github.com/google-deepmind/tapnet](https://github.com/google-deepmind/tapnet)
## Included Models
[**TAPIR**](https://deepmind-tapir.github.io/) – A fast and accurate point tracker for continuous point trajectories in space-time, presented in the paper [**TAPIR: Tracking Any Point with per-frame Initialization and temporal Refinement**](https://huggingface.co/papers/2306.08637).
[**BootsTAPIR**](https://bootstap.github.io/) – A bootstrapped variant of TAPIR that improves robustness and stability across long videos via self-supervised refinement, presented in the paper [**BootsTAP: Bootstrapped Training for Tracking-Any-Point**](https://huggingface.co/papers/2402.00847).
[**TAPNext**](https://tap-next.github.io/) – A new generative approach that frames point tracking as next-token prediction, enabling semi-dense, accurate, and temporally coherent tracking across challenging videos, presented in the paper [**TAPNext: Tracking Any Point (TAP) as Next Token Prediction**](https://huggingface.co/papers/2504.05579).
These models provide state-of-the-art performance for tracking arbitrary points in videos and support research and applications in robotics, perception, and video generation.
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