We introduce HOIGPT, a token-based generative method that unifies 3D hand-object interactions (HOI) perception and generation, offering the first comprehensive solution for captioning and generating high-quality 3D HOI sequences from a diverse range of conditional signals (\eg text, objects, partial sequences). At its core, HOIGPT utilizes a large language model to predict the bidrectional transformation between HOI sequences and natural language descriptions. Given text inputs, HOIGPT generates a sequence of hand and object meshes; given (partial) HOI sequences, HOIGPT generates text descriptions and completes the sequences. To facilitate HOI understanding with a large language model, this paper introduces two key innovations: (1) a novel physically grounded HOI tokenizer, the hand-object decomposed VQ-VAE, for discretizing HOI sequences, and (2) a motion-aware language model trained to process and generate both text and HOI tokens. Extensive experiments demonstrate that HOIGPT sets new state-of-the-art performance on both text generation (+2.01% R Precision) and HOI generation (-2.56 FID) across multiple tasks and benchmarks.
View on arXiv@article{huang2025_2503.19157,
title={ HOIGPT: Learning Long Sequence Hand-Object Interaction with Language Models },
author={ Mingzhen Huang and Fu-Jen Chu and Bugra Tekin and Kevin J Liang and Haoyu Ma and Weiyao Wang and Xingyu Chen and Pierre Gleize and Hongfei Xue and Siwei Lyu and Kris Kitani and Matt Feiszli and Hao Tang },
journal={arXiv preprint arXiv:2503.19157},
year={ 2025 }
}