SIMS: Simulating Human-Scene Interactions with Real World Script Planning

• URL: http://arxiv.org/abs/2411.19921v1
• Date: Fri, 29 Nov 2024 18:36:15 GMT
• Title: SIMS: Simulating Human-Scene Interactions with Real World Script Planning
• Authors: Wenjia Wang, Liang Pan, Zhiyang Dou, Zhouyingcheng Liao, Yuke Lou, Lei Yang, Jingbo Wang, Taku Komura,
• Abstract summary: This paper introduces a novel framework for the planning and controlling of long-horizon physical plausible human-scene interaction. Large Language Models (LLMs) can understand and generate logical storylines. By leveraging this, we utilize a dual-aware policy that achieves both language comprehension and scene understanding.
• Score: 33.31213669502036
• License:
• Abstract: Simulating long-term human-scene interaction is a challenging yet fascinating task. Previous works have not effectively addressed the generation of long-term human scene interactions with detailed narratives for physics-based animation. This paper introduces a novel framework for the planning and controlling of long-horizon physical plausible human-scene interaction. On the one hand, films and shows with stylish human locomotions or interactions with scenes are abundantly available on the internet, providing a rich source of data for script planning. On the other hand, Large Language Models (LLMs) can understand and generate logical storylines. This motivates us to marry the two by using an LLM-based pipeline to extract scripts from videos, and then employ LLMs to imitate and create new scripts, capturing complex, time-series human behaviors and interactions with environments. By leveraging this, we utilize a dual-aware policy that achieves both language comprehension and scene understanding to guide character motions within contextual and spatial constraints. To facilitate training and evaluation, we contribute a comprehensive planning dataset containing diverse motion sequences extracted from real-world videos and expand them with large language models. We also collect and re-annotate motion clips from existing kinematic datasets to enable our policy learn diverse skills. Extensive experiments demonstrate the effectiveness of our framework in versatile task execution and its generalization ability to various scenarios, showing remarkably enhanced performance compared with existing methods. Our code and data will be publicly available soon.

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