We propose a novel approach for generating text-guided human-object interactions (HOIs) that achieves explicit joint-level interaction modeling in a computationally efficient manner. Previous methods represent the entire human body as a single token, making it difficult to capture fine-grained joint-level interactions and resulting in unrealistic HOIs. However, treating each individual joint as a token would yield over twenty times more tokens, increasing computational overhead. To address these challenges, we introduce an Efficient Explicit Joint-level Interaction Model (EJIM). EJIM features a Dual-branch HOI Mamba that separately and efficiently models spatiotemporal HOI information, as well as a Dual-branch Condition Injector for integrating text semantics and object geometry into human and object motions. Furthermore, we design a Dynamic Interaction Block and a progressive masking mechanism to iteratively filter out irrelevant joints, ensuring accurate and nuanced interaction modeling. Extensive quantitative and qualitative evaluations on public datasets demonstrate that EJIM surpasses previous works by a large margin while using only 5\% of the inference time. Code is available \href{this https URL}{here}.
View on arXiv@article{huang2025_2503.23121,
title={ Efficient Explicit Joint-level Interaction Modeling with Mamba for Text-guided HOI Generation },
author={ Guohong Huang and Ling-An Zeng and Zexin Zheng and Shengbo Gu and Wei-Shi Zheng },
journal={arXiv preprint arXiv:2503.23121},
year={ 2025 }
}