HOI-M3:Capture Multiple Humans and Objects Interaction within Contextual Environment
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913294895284224 |
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| author | Zhang, Juze Zhang, Jingyan Song, Zining Shi, Zhanhe Zhao, Chengfeng Shi, Ye Yu, Jingyi Xu, Lan Wang, Jingya |
| author_facet | Zhang, Juze Zhang, Jingyan Song, Zining Shi, Zhanhe Zhao, Chengfeng Shi, Ye Yu, Jingyi Xu, Lan Wang, Jingya |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_00299 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HOI-M3:Capture Multiple Humans and Objects Interaction within Contextual Environment Zhang, Juze Zhang, Jingyan Song, Zining Shi, Zhanhe Zhao, Chengfeng Shi, Ye Yu, Jingyi Xu, Lan Wang, Jingya Computer Vision and Pattern Recognition 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. |
| title | HOI-M3:Capture Multiple Humans and Objects Interaction within Contextual Environment |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.00299 |