HAIC: Improving Human Action Understanding and Generation with Better
  Captions for Multi-modal Large Language Models
Authors: name: Xiao Wang, name: Jingyun Hua, name: Weihong Lin, name: Yuanxing Zhang, name: Fuzheng Zhang, name: Jianlong Wu, name: Di Zhang, name: Liqiang Nie

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. HAICTrain comprises 126K video-caption pairs
generated by Gemini-Pro and verified for training purposes. Meanwhile,
HAICBench includes 500 manually annotated video-caption pairs and
1,400 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 are released at https://huggingface.co/datasets/KuaishouHAIC/HAIC.