Efficient Explicit Joint-level Interaction Modeling with Mamba for Text-guided HOI Generation

• URL: http://arxiv.org/abs/2503.23121v1
• Date: Sat, 29 Mar 2025 15:23:21 GMT
• Title: Efficient Explicit Joint-level Interaction Modeling with Mamba for Text-guided HOI Generation
• Abstract summary: We introduce an Efficient Explicit Joint-level Interaction Model (EJIM) for generating text-guided human-object interactions.<n>EJIM features a Dual-branch HOI Mamba that separately and efficiently models human object and motions.<n>We show that EJIM surpasses previous works by a large margin while using only 5% of the inference time.
• Score: 25.770855154106453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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{https://github.com/Huanggh531/EJIM}{here}.

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