Human-Object Interaction with Vision-Language Model Guided Relative Movement Dynamics

• URL: http://arxiv.org/abs/2503.18349v1
• Date: Mon, 24 Mar 2025 05:18:04 GMT
• Title: Human-Object Interaction with Vision-Language Model Guided Relative Movement Dynamics
• Authors: Zekai Deng, Ye Shi, Kaiyang Ji, Lan Xu, Shaoli Huang, Jingya Wang,
• Abstract summary: This paper introduces a unified Human-Object Interaction framework.<n>It provides unified control over interactions with static scenes and dynamic objects using language commands.<n>Our framework supports long-horizon interactions among dynamic, articulated, and static objects.
• Score: 30.43930233035367
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human-Object Interaction (HOI) is vital for advancing simulation, animation, and robotics, enabling the generation of long-term, physically plausible motions in 3D environments. However, existing methods often fall short of achieving physics realism and supporting diverse types of interactions. To address these challenges, this paper introduces a unified Human-Object Interaction framework that provides unified control over interactions with static scenes and dynamic objects using language commands. The interactions between human and object parts can always be described as the continuous stable Relative Movement Dynamics (RMD) between human and object parts. By leveraging the world knowledge and scene perception capabilities of Vision-Language Models (VLMs), we translate language commands into RMD diagrams, which are used to guide goal-conditioned reinforcement learning for sequential interaction with objects. Our framework supports long-horizon interactions among dynamic, articulated, and static objects. To support the training and evaluation of our framework, we present a new dataset named Interplay, which includes multi-round task plans generated by VLMs, covering both static and dynamic HOI tasks. Extensive experiments demonstrate that our proposed framework can effectively handle a wide range of HOI tasks, showcasing its ability to maintain long-term, multi-round transitions. For more details, please refer to our project webpage: https://rmd-hoi.github.io/.

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