HOI-M3:Capture Multiple Humans and Objects Interaction within Contextual Environment

• URL: http://arxiv.org/abs/2404.00299v2
• Date: Tue, 2 Apr 2024 12:34:09 GMT
• Title: HOI-M3:Capture Multiple Humans and Objects Interaction within Contextual Environment
• Authors: Juze Zhang, Jingyan Zhang, Zining Song, Zhanhe Shi, Chengfeng Zhao, Ye Shi, Jingyi Yu, Lan Xu, Jingya Wang,
• Abstract summary: HOI-M3 is a novel large-scale dataset for modeling the interactions of Multiple huMans and Multiple objects. It provides accurate 3D tracking for both humans and objects from dense RGB and object-mounted IMU inputs.
• Score: 43.6454394625555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humans naturally interact with both others and the surrounding multiple objects, engaging in various social activities. However, recent advances in modeling human-object interactions mostly focus on perceiving isolated individuals and objects, due to fundamental data scarcity. In this paper, we introduce HOI-M3, a novel large-scale dataset for modeling the interactions of Multiple huMans and Multiple objects. Notably, it provides accurate 3D tracking for both humans and objects from dense RGB and object-mounted IMU inputs, covering 199 sequences and 181M frames of diverse humans and objects under rich activities. With the unique HOI-M3 dataset, we introduce two novel data-driven tasks with companion strong baselines: monocular capture and unstructured generation of multiple human-object interactions. Extensive experiments demonstrate that our dataset is challenging and worthy of further research about multiple human-object interactions and behavior analysis. Our HOI-M3 dataset, corresponding codes, and pre-trained models will be disseminated to the community for future research.

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