Dingbang Huang — accepted AI-conference papers

2026

Recovering Physically Plausible Human-Object Interactions from Monocular Videos

Dingbang Huang, Etienne Vouga, Qixing Huang, Georgios Pavlakos

CVPR 2026

In this paper, we present a method to reconstruct physically plausible human-object interactions (HOI) from monocular videos. While existing kinematic-based approaches produce visually plausible motion, they often result in physical artifacts such as interpenetration and object floating. To overcome

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2025

Reconstructing In-the-Wild Open-Vocabulary Human-Object Interactions

Boran Wen, Dingbang Huang, Zichen Zhang, Jiahong Zhou, Jianbin Deng, Jingyu Gong +3

CVPR 2025poster

Reconstructing human-object interactions (HOI) from single images is fundamental in computer vision. Existing methods are primarily trained and tested on indoor scenes due to the lack of 3D data, particularly constrained by the object variety, making it challenging to generalize to real-world scenes…

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