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README.md

@@ -1,6 +1,51 @@
-# UniBallRacket Dataset
+---
+license: mit
+language:
+  - en
+pretty_name: RacketVision Dataset
+size_categories:
+  - 10K<n<100K
+task_categories:
+  - object-detection
+  - video-classification
+tags:
+  - sports-analytics
+  - computer-vision
+  - object-tracking
+  - trajectory-prediction
+  - ball-tracking
+  - racket-pose-estimation
+  - badminton
+  - table-tennis
+  - tennis
+  - racket-sports
+---
+
+# RacketVision Dataset
+
+[![Arxiv](https://img.shields.io/badge/ArXiv-2511.17045-B31B1B.svg)](https://arxiv.org/abs/2511.17045)
+[![AAAI](https://img.shields.io/badge/AAAI_2026-Oral-blue.svg)](https://aaai.org/)
+[![GitHub](https://img.shields.io/badge/GitHub-Code-black?logo=github)](https://github.com/OrcustD/RacketVision/)
+[![Hugging Face Dataset](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-blue)](https://huggingface.co/datasets/linfeng302/RacketVision)
+[![Hugging Face Models](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-yellow)](https://huggingface.co/linfeng302/RacketVision-Models)
+[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://github.com/OrcustD/RacketVision/blob/main/LICENSE)
+
+**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**.
+
+- **Paper:** [arXiv:2511.17045](https://arxiv.org/abs/2511.17045)
+- **Code:** [github.com/OrcustD/RacketVision](https://github.com/OrcustD/RacketVision/)
+
+![Teaser](https://raw.githubusercontent.com/OrcustD/RacketVision/main/assets/teaser.jpg)
+
+## Using this Hub repository
+
+This dataset is distributed as **static files** (videos, CSV, JSON, PKL). Download it with the Hugging Face CLI, then follow the [project README](https://github.com/OrcustD/RacketVision/blob/main/README.md) for environment setup and training:
 
-This directory contains the dataset for the UniBallRacket framework, covering three racket sports: **badminton**, **table tennis**, and **tennis**.
+```bash
+hf download linfeng302/RacketVision --repo-type dataset --local-dir data
+```
+
+The in-browser Dataset Viewer may not fully load all assets: COCO detection and pose JSON files use different annotation schemas, so they are not merged into a single `datasets`-style table. Use the files on disk as documented below.
 
 ## Directory Layout
 
@@ -22,10 +67,10 @@ data/
 │   │   └── <match>_<rally>.mp4    # Raw video clips
 │   ├── all/
 │   │   └── <match>/
-│   │       ├── median.npz         # Median background frame (for BallTrack)
-│   │       ├── frame/<rally>/     # Extracted JPG frames
 │   │       ├── csv/<rally>_ball.csv        # Ball ground truth annotations
 │   │       └── racket/<rally>/*.json       # Racket ground truth annotations
+│   ├── interp_ball/               # Interpolated ball trajectories (for rebuilding TrajPred data)
+│   ├── merged_racket/             # Merged racket predictions (for rebuilding TrajPred data)
 │   └── info/
 │       ├── metainfo.json          # Sport-specific metadata
 │       ├── train.json             # [[match_id, rally_id], ...] for training
@@ -37,6 +82,8 @@ data/
     └── ball_racket_<sport>_h80_f20.pkl   # Long-horizon: 80 history → 20 future
 ```
 
+**Local preprocessing (required for BallTrack):** after download, generate per-match `frame/<rally>/` (JPG frames) and `median.npz` from the videos using `DataPreprocess/extract_frames.py` and `DataPreprocess/create_median.py`. These are omitted from the Hub release to save space; see the [project README](https://github.com/OrcustD/RacketVision/blob/main/README.md).
+
 ## Data Formats
 
 ### Ball Annotations (`csv/<rally>_ball.csv`)
@@ -117,7 +164,7 @@ JSON list of `[match_id, rally_id]` pairs:
 
 ## Generating Data from Scratch
 
-If you have the raw videos, use `DataPreprocess/` scripts to prepare all intermediate files:
+If you have the raw videos, use `DataPreprocess/` scripts in the [code repository](https://github.com/OrcustD/RacketVision/) to prepare all intermediate files:
 
 ```bash
 cd DataPreprocess
@@ -151,3 +198,16 @@ python merge_gt_with_predictions.py --data_root ../data --sport badminton
 # Build PKL dataset
 python build_dataset.py --data_root ../data --sport badminton --history 80 --future 20
 ```
+
+## Citation
+
+If you find this work useful, please consider citing:
+
+```bibtex
+@inproceedings{dong2026racket,
+  title={Racket Vision: A Multiple Racket Sports Benchmark for Unified Ball and Racket Analysis},
+  author={Dong, Linfeng and Yang, Yuchen and Wu, Hao and Wang, Wei and Hou, Yuenan and Zhong, Zhihang and Sun, Xiao},
+  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},
+  year={2026}
+}
+```