Unified Human-Scene Interaction via Prompted Chain-of-Contacts
This work addresses the need for practical HSI applications in fields like embodied AI and virtual reality, representing a novel method for a known bottleneck.
The paper tackles the problem of versatile interaction control and user-friendly interfaces in Human-Scene Interaction (HSI) by introducing UniHSI, a framework that uses language commands to control diverse interactions via a Chain of Contacts (CoC) approach, achieving effective task execution and generalizability to real scanned scenes.
Human-Scene Interaction (HSI) is a vital component of fields like embodied AI and virtual reality. Despite advancements in motion quality and physical plausibility, two pivotal factors, versatile interaction control and the development of a user-friendly interface, require further exploration before the practical application of HSI. This paper presents a unified HSI framework, UniHSI, which supports unified control of diverse interactions through language commands. This framework is built upon the definition of interaction as Chain of Contacts (CoC): steps of human joint-object part pairs, which is inspired by the strong correlation between interaction types and human-object contact regions. Based on the definition, UniHSI constitutes a Large Language Model (LLM) Planner to translate language prompts into task plans in the form of CoC, and a Unified Controller that turns CoC into uniform task execution. To facilitate training and evaluation, we collect a new dataset named ScenePlan that encompasses thousands of task plans generated by LLMs based on diverse scenarios. Comprehensive experiments demonstrate the effectiveness of our framework in versatile task execution and generalizability to real scanned scenes. The project page is at https://github.com/OpenRobotLab/UniHSI .