My research focuses on advancing machine learning, particularly generative models, and their applications in biological sciences and beyond. I am also developing expertise in reinforcement learning algorithms to solve complex optimization problems.
While large-scale human motion capture datasets have advanced human motion generation, modeling and generating dynamic 3D human-object interactions (HOIs) remains challenging due to dataset limitations. These datasets often lack extensive, high-quality text-interaction pair data and exhibit artifacts such as contact penetration, floating, and incorrect hand motions. To address these issues, we introduce InterAct, a large-scale 3D HOI benchmark with key contributions in both dataset and methodology. First, we consolidate 21.81 hours of HOI data from diverse sources, standardizing and enriching them with detailed textual annotations. Second, we propose a unified optimization framework that enhances data quality by minimizing artifacts and restoring hand motions. Leveraging the insight of contact invariance, we preserve human-object relationships while introducing motion variations, thereby expanding the dataset to 30.70 hours. Third, we introduce six tasks to benchmark existing methods and develop a unified HOI generative model based on multi-task learning that achieves state-of-the-art results. Extensive experiments validate the utility of our dataset as a foundational resource for advancing 3D human-object interaction generation. The dataset will be publicly accessible to support further research in the field.
@inproceedings{xu2025interact,title={InterAct: Advancing Large-Scale Versatile 3D Human-Object Interaction Generation},author={Xu, Sirui and Li, Dongting and Zhang, Yucheng and Xu, Xiyan and Long, Qi and Wang, Ziyin and Lu, Yunzhi and Dong, Shuchang and Jiang, Hezi and Gupta, Akshat and Wang, Yu-Xiong and Gui, Liang-Yan},booktitle={CVPR},year={2025},}
