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  [![GitHub](https://img.shields.io/badge/GitHub-Code-black?logo=github)](https://github.com/OrcustD/RacketVision/)
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  [![Hugging Face Dataset](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-blue)](https://huggingface.co/datasets/linfeng302/RacketVision)
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  [![Hugging Face Models](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-yellow)](https://huggingface.co/linfeng302/RacketVision-Models)
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- [![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://github.com/OrcustD/RacketVision/blob/main/LICENSE)
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  **RacketVision** is a large-scale, multi-sport dataset and benchmark for advancing computer vision in sports analytics, covering **badminton**, **table tennis**, and **tennis**. It is the first dataset to provide large-scale, fine-grained annotations for racket pose alongside traditional ball positions, enabling research into complex human-object interactions. The benchmark tackles three interconnected tasks: fine-grained **ball tracking**, articulated **racket pose estimation**, and predictive ball **trajectory forecasting**.
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- - **Paper:** [arXiv:2511.17045](https://arxiv.org/abs/2511.17045)
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- - **Code:** [github.com/OrcustD/RacketVision](https://github.com/OrcustD/RacketVision/)
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  ![Teaser](https://raw.githubusercontent.com/OrcustD/RacketVision/main/assets/teaser.jpg)
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  ## Using this Hub repository
 
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  [![GitHub](https://img.shields.io/badge/GitHub-Code-black?logo=github)](https://github.com/OrcustD/RacketVision/)
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  [![Hugging Face Dataset](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-blue)](https://huggingface.co/datasets/linfeng302/RacketVision)
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  [![Hugging Face Models](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-yellow)](https://huggingface.co/linfeng302/RacketVision-Models)
 
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  **RacketVision** is a large-scale, multi-sport dataset and benchmark for advancing computer vision in sports analytics, covering **badminton**, **table tennis**, and **tennis**. It is the first dataset to provide large-scale, fine-grained annotations for racket pose alongside traditional ball positions, enabling research into complex human-object interactions. The benchmark tackles three interconnected tasks: fine-grained **ball tracking**, articulated **racket pose estimation**, and predictive ball **trajectory forecasting**.
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  ![Teaser](https://raw.githubusercontent.com/OrcustD/RacketVision/main/assets/teaser.jpg)
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  ## Using this Hub repository