File size: 2,210 Bytes
c14d9b0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | # 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:
```text
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
```bibtex
@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}
}
```
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