Datasets:

gouba2333
/

BoxComm-Dataset

BoxComm-Dataset is the official data release for BoxComm, a benchmark for category-aware boxing commentary generation and narration-rhythm evaluation.

Resources

Project Page: https://gouba2333.github.io/BoxComm
Paper: http://arxiv.org/abs/2604.04419
Code: https://github.com/gouba2333/BoxComm
Benchmark: https://huggingface.co/datasets/gouba2333/BoxComm

Overview

This dataset release is intended for training, analysis, and reproducible preprocessing. It contains the complete processed videos together with the released annotations and benchmark metadata.

Recommended structure:

BoxComm-Dataset/
├── train/
│   ├── videos/
│   ├── events/
│   └── asr/
├── eval/
│   ├── videos/
│   ├── events/
│   └── asr/
└── metadata/

The split convention is:

train: video id < 478
eval: video id >= 478

Each event directory should contain:

one skeleton .pkl file
one video_event_inference_3.json file

Each ASR JSON file should contain classified_segments.

What is included

processed match videos
event annotations
skeleton data
ASR with sentence segmentation
3-way commentary labels
split metadata

Intended uses

supervised fine-tuning for commentary generation
category-aware commentary evaluation
narration-rhythm analysis
multimodal sports video understanding research

Data preparation in the code repository

The official code repository provides:

scripts/prep_qwen3vl_sft_data.py
scripts/train_qwen3vl.py
scripts/infer_qwen3vl.py
scripts/eval_metrics.py
scripts/eval_streaming_cls_metrics.py

Repository: https://github.com/gouba2333/BoxComm

Licensing

The public release includes processed videos, ASR annotations, event JSON files, skeleton PKL files, and benchmark metadata for research use.

Citation

@article{wang2026boxcomm,
  title={BoxComm: Benchmarking Category-Aware Commentary Generation and Narration Rhythm in Boxing},
  author={Wang, Kaiwen and Zheng, Kaili and Deng, Rongrong and Shi, Yiming and Guo, Chenyi and Wu, Ji},
  journal={arXiv preprint arXiv:2604.04419},
  year={2026}
}