@inproceedings{wang-etal-2025-haic,
    title = "{HAIC}: Improving Human Action Understanding and Generation with Better Captions for Multi-modal Large Language Models",
    author = "Wang, Xiao  and
      Hua, Jingyun  and
      Lin, Weihong  and
      Zhang, Yuanxing  and
      Zhang, Fuzheng  and
      Wu, Jianlong  and
      Zhang, Di  and
      Nie, Liqiang",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/revision-previews/2025.acl-long.501/",
    doi = "10.18653/v1/2025.acl-long.501",
    pages = "10158--10181",
    ISBN = "979-8-89176-251-0",
    abstract = "Recent Multi-modal Large Language Models (MLLMs) have made great progress in video understanding. However, their performance on videos involving human actions is still limited by the lack of high-quality data. To address this, we introduce a two-stage data annotation pipeline. First, we design strategies to accumulate videos featuring clear human actions from the Internet. Second, videos are annotated in a standardized caption format that uses human attributes to distinguish individuals and chronologically details their actions and interactions. Through this pipeline, we curate two datasets, namely HAICTrain and HAICBench. \textbf{HAICTrain} comprises 126K video-caption pairs generated by Gemini-Pro and verified for training purposes. Meanwhile, \textbf{HAICBench} includes 412 manually annotated video-caption pairs and 2,000 QA pairs, for a comprehensive evaluation of human action understanding. Experimental results demonstrate that training with HAICTrain not only significantly enhances human understanding abilities across 4 benchmarks, but can also improve text-to-video generation results. Both the HAICTrain and HAICBench will be made open-source to facilitate further research."
}