Update README.md

README.md

@@ -13,16 +13,10 @@ This repository contains the checkpoints of several point tracking models develo
 
 ## Included Models
 
-- **TAPIR** – A fast and accurate point tracker for continuous point trajectories in space-time.
+[**TAPIR**](https://deepmind-tapir.github.io/) – 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/)
+[**BootsTAPIR**](https://bootstap.github.io/) – A bootstrapped variant of TAPIR that improves robustness and stability across long videos via self-supervised refinement.
   
-- **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/)
+[**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, including those 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.