I'M HOI: Inertia-aware Monocular Capture of 3D Human-Object Interactions

• URL: http://arxiv.org/abs/2312.08869v2
• Date: Sat, 30 Mar 2024 07:23:20 GMT
• Title: I'M HOI: Inertia-aware Monocular Capture of 3D Human-Object Interactions
• Authors: Chengfeng Zhao, Juze Zhang, Jiashen Du, Ziwei Shan, Junye Wang, Jingyi Yu, Jingya Wang, Lan Xu,
• Abstract summary: I'm-HOI is a monocular scheme to faithfully capture the 3D motions of both the human and object in a novel setting. It combines general motion inference and category-aware refinement. Our dataset and code will be released to the community.
• Score: 42.87514729260336
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We are living in a world surrounded by diverse and "smart" devices with rich modalities of sensing ability. Conveniently capturing the interactions between us humans and these objects remains far-reaching. In this paper, we present I'm-HOI, a monocular scheme to faithfully capture the 3D motions of both the human and object in a novel setting: using a minimal amount of RGB camera and object-mounted Inertial Measurement Unit (IMU). It combines general motion inference and category-aware refinement. For the former, we introduce a holistic human-object tracking method to fuse the IMU signals and the RGB stream and progressively recover the human motions and subsequently the companion object motions. For the latter, we tailor a category-aware motion diffusion model, which is conditioned on both the raw IMU observations and the results from the previous stage under over-parameterization representation. It significantly refines the initial results and generates vivid body, hand, and object motions. Moreover, we contribute a large dataset with ground truth human and object motions, dense RGB inputs, and rich object-mounted IMU measurements. Extensive experiments demonstrate the effectiveness of I'm-HOI under a hybrid capture setting. Our dataset and code will be released to the community.

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