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README.md

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 # TAPNet
 
-This repository contains the models presented in [TAPNext: Tracking Any Point (TAP) as Next Token Prediction](https://huggingface.co/papers/2504.05579).
+This repository contains the checkpoints of several point tracking models developed by DeepMind for point tracking.
 
-Code: https://github.com/google-deepmind/tapnet
+🔗 **Code**: [https://github.com/google-deepmind/tapnet](https://github.com/google-deepmind/tapnet)  
 
-Project page: https://tap-next.github.io/
+## Included Models
+
+- **TAPIR** (*Tracking Any Point with Implicit Representations*) – A fast and accurate point tracker for continuous point trajectories in space-time.
+🌐 **Project page**: [https://deepmind-tapir.github.io/](https://deepmind-tapir.github.io/)
+
+- **BootsTAPIR** – A bootstrapped variant of TAPIR that improves robustness and stability across long videos via self-supervised refinement.
+🌐 **Project page**: [https://bootstap.github.io/](https://bootstap.github.io/)
+  
+- **TAPNext** – A new generative approach that frames point tracking as next-token prediction, enabling semi-dense, accurate, and temporally coherent tracking across challenging videos, including those presented in the paper [**TAPNext: Tracking Any Point (TAP) as Next Token Prediction**](https://huggingface.co/papers/2504.05579).
+🌐 **Project page**: [https://tap-next.github.io/](https://tap-next.github.io/)
+
+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.