OpenHOI: Open-World Hand-Object Interaction Synthesis with Multimodal Large Language Model

• URL: http://arxiv.org/abs/2505.18947v1
• Date: Sun, 25 May 2025 02:48:43 GMT
• Title: OpenHOI: Open-World Hand-Object Interaction Synthesis with Multimodal Large Language Model
• Authors: Zhenhao Zhang, Ye Shi, Lingxiao Yang, Suting Ni, Qi Ye, Jingya Wang,
• Abstract summary: We introduce OpenHOI, the first framework for open-world HOI synthesis.<n>Our approach integrates a 3D Multimodal Large Language Model (MLLM) fine-tuned for joint affordance grounding and semantic task decomposition.<n>To synthesize physically plausible interactions, we propose an affordance-driven diffusion model paired with a training-free physics refinement stage.
• Score: 22.545267010077822
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding and synthesizing realistic 3D hand-object interactions (HOI) is critical for applications ranging from immersive AR/VR to dexterous robotics. Existing methods struggle with generalization, performing well on closed-set objects and predefined tasks but failing to handle unseen objects or open-vocabulary instructions. We introduce OpenHOI, the first framework for open-world HOI synthesis, capable of generating long-horizon manipulation sequences for novel objects guided by free-form language commands. Our approach integrates a 3D Multimodal Large Language Model (MLLM) fine-tuned for joint affordance grounding and semantic task decomposition, enabling precise localization of interaction regions (e.g., handles, buttons) and breakdown of complex instructions (e.g., "Find a water bottle and take a sip") into executable sub-tasks. To synthesize physically plausible interactions, we propose an affordance-driven diffusion model paired with a training-free physics refinement stage that minimizes penetration and optimizes affordance alignment. Evaluations across diverse scenarios demonstrate OpenHOI's superiority over state-of-the-art methods in generalizing to novel object categories, multi-stage tasks, and complex language instructions. Our project page at \href{https://openhoi.github.io}

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