CIRCLE: Capture In Rich Contextual Environments

• URL: http://arxiv.org/abs/2303.17912v1
• Date: Fri, 31 Mar 2023 09:18:12 GMT
• Title: CIRCLE: Capture In Rich Contextual Environments
• Authors: Joao Pedro Araujo, Jiaman Li, Karthik Vetrivel, Rishi Agarwal, Deepak Gopinath, Jiajun Wu, Alexander Clegg, C. Karen Liu
• Abstract summary: We propose a novel motion acquisition system in which the actor perceives and operates in a highly contextual virtual world. We present CIRCLE, a dataset containing 10 hours of full-body reaching motion from 5 subjects across nine scenes. We use this dataset to train a model that generates human motion conditioned on scene information.
• Score: 69.97976304918149
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Synthesizing 3D human motion in a contextual, ecological environment is important for simulating realistic activities people perform in the real world. However, conventional optics-based motion capture systems are not suited for simultaneously capturing human movements and complex scenes. The lack of rich contextual 3D human motion datasets presents a roadblock to creating high-quality generative human motion models. We propose a novel motion acquisition system in which the actor perceives and operates in a highly contextual virtual world while being motion captured in the real world. Our system enables rapid collection of high-quality human motion in highly diverse scenes, without the concern of occlusion or the need for physical scene construction in the real world. We present CIRCLE, a dataset containing 10 hours of full-body reaching motion from 5 subjects across nine scenes, paired with ego-centric information of the environment represented in various forms, such as RGBD videos. We use this dataset to train a model that generates human motion conditioned on scene information. Leveraging our dataset, the model learns to use ego-centric scene information to achieve nontrivial reaching tasks in the context of complex 3D scenes. To download the data please visit https://stanford-tml.github.io/circle_dataset/.

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