COUCH: Towards Controllable Human-Chair Interactions

• URL: http://arxiv.org/abs/2205.00541v1
• Date: Sun, 1 May 2022 19:14:22 GMT
• Title: COUCH: Towards Controllable Human-Chair Interactions
• Authors: Xiaohan Zhang, Bharat Lal Bhatnagar, Vladimir Guzov, Sebastian Starke, Gerard Pons-Moll
• Abstract summary: We study the problem of synthesizing scene interactions conditioned on different contact positions on the object. We propose a novel synthesis framework COUCH that plans ahead the motion by predicting contact-aware control signals of the hands. Our method shows significant quantitative and qualitative improvements over existing methods for human-object interactions.
• Score: 44.66450508317131
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Humans interact with an object in many different ways by making contact at different locations, creating a highly complex motion space that can be difficult to learn, particularly when synthesizing such human interactions in a controllable manner. Existing works on synthesizing human scene interaction focus on the high-level control of action but do not consider the fine-grained control of motion. In this work, we study the problem of synthesizing scene interactions conditioned on different contact positions on the object. As a testbed to investigate this new problem, we focus on human-chair interaction as one of the most common actions which exhibit large variability in terms of contacts. We propose a novel synthesis framework COUCH that plans ahead the motion by predicting contact-aware control signals of the hands, which are then used to synthesize contact-conditioned interactions. Furthermore, we contribute a large human-chair interaction dataset with clean annotations, the COUCH Dataset. Our method shows significant quantitative and qualitative improvements over existing methods for human-object interactions. More importantly, our method enables control of the motion through user-specified or automatically predicted contacts.

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