HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| _version_ | 1866911745841299456 |
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| author | Liu, Yunze Liu, Yun Jiang, Che Lyu, Kangbo Wan, Weikang Shen, Hao Liang, Boqiang Fu, Zhoujie Wang, He Yi, Li |
| author_facet | Liu, Yunze Liu, Yun Jiang, Che Lyu, Kangbo Wan, Weikang Shen, Hao Liang, Boqiang Fu, Zhoujie Wang, He Yi, Li |
| contents | We present HOI4D, a large-scale 4D egocentric dataset with rich annotations, to catalyze the research of category-level human-object interaction. HOI4D consists of 2.4M RGB-D egocentric video frames over 4000 sequences collected by 4 participants interacting with 800 different object instances from 16 categories over 610 different indoor rooms. Frame-wise annotations for panoptic segmentation, motion segmentation, 3D hand pose, category-level object pose and hand action have also been provided, together with reconstructed object meshes and scene point clouds. With HOI4D, we establish three benchmarking tasks to promote category-level HOI from 4D visual signals including semantic segmentation of 4D dynamic point cloud sequences, category-level object pose tracking, and egocentric action segmentation with diverse interaction targets. In-depth analysis shows HOI4D poses great challenges to existing methods and produces great research opportunities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2203_01577 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction Liu, Yunze Liu, Yun Jiang, Che Lyu, Kangbo Wan, Weikang Shen, Hao Liang, Boqiang Fu, Zhoujie Wang, He Yi, Li Computer Vision and Pattern Recognition We present HOI4D, a large-scale 4D egocentric dataset with rich annotations, to catalyze the research of category-level human-object interaction. HOI4D consists of 2.4M RGB-D egocentric video frames over 4000 sequences collected by 4 participants interacting with 800 different object instances from 16 categories over 610 different indoor rooms. Frame-wise annotations for panoptic segmentation, motion segmentation, 3D hand pose, category-level object pose and hand action have also been provided, together with reconstructed object meshes and scene point clouds. With HOI4D, we establish three benchmarking tasks to promote category-level HOI from 4D visual signals including semantic segmentation of 4D dynamic point cloud sequences, category-level object pose tracking, and egocentric action segmentation with diverse interaction targets. In-depth analysis shows HOI4D poses great challenges to existing methods and produces great research opportunities. |
| title | HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2203.01577 |