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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.15898 |
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| _version_ | 1866909545339551744 |
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| author | Wen, Boran Huang, Dingbang Zhang, Zichen Zhou, Jiahong Deng, Jianbin Gong, Jingyu Chen, Yulong Ma, Lizhuang Li, Yong-Lu |
| author_facet | Wen, Boran Huang, Dingbang Zhang, Zichen Zhou, Jiahong Deng, Jianbin Gong, Jingyu Chen, Yulong Ma, Lizhuang Li, Yong-Lu |
| contents | Reconstructing human-object interactions (HOI) from single images is fundamental in computer vision. Existing methods are primarily trained and tested on indoor scenes due to the lack of 3D data, particularly constrained by the object variety, making it challenging to generalize to real-world scenes with a wide range of objects. The limitations of previous 3D HOI datasets were primarily due to the difficulty in acquiring 3D object assets. However, with the development of 3D reconstruction from single images, recently it has become possible to reconstruct various objects from 2D HOI images. We therefore propose a pipeline for annotating fine-grained 3D humans, objects, and their interactions from single images. We annotated 2.5k+ 3D HOI assets from existing 2D HOI datasets and built the first open-vocabulary in-the-wild 3D HOI dataset Open3DHOI, to serve as a future test set. Moreover, we design a novel Gaussian-HOI optimizer, which efficiently reconstructs the spatial interactions between humans and objects while learning the contact regions. Besides the 3D HOI reconstruction, we also propose several new tasks for 3D HOI understanding to pave the way for future work. Data and code will be publicly available at https://wenboran2002.github.io/3dhoi. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15898 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reconstructing In-the-Wild Open-Vocabulary Human-Object Interactions Wen, Boran Huang, Dingbang Zhang, Zichen Zhou, Jiahong Deng, Jianbin Gong, Jingyu Chen, Yulong Ma, Lizhuang Li, Yong-Lu Computer Vision and Pattern Recognition Reconstructing human-object interactions (HOI) from single images is fundamental in computer vision. Existing methods are primarily trained and tested on indoor scenes due to the lack of 3D data, particularly constrained by the object variety, making it challenging to generalize to real-world scenes with a wide range of objects. The limitations of previous 3D HOI datasets were primarily due to the difficulty in acquiring 3D object assets. However, with the development of 3D reconstruction from single images, recently it has become possible to reconstruct various objects from 2D HOI images. We therefore propose a pipeline for annotating fine-grained 3D humans, objects, and their interactions from single images. We annotated 2.5k+ 3D HOI assets from existing 2D HOI datasets and built the first open-vocabulary in-the-wild 3D HOI dataset Open3DHOI, to serve as a future test set. Moreover, we design a novel Gaussian-HOI optimizer, which efficiently reconstructs the spatial interactions between humans and objects while learning the contact regions. Besides the 3D HOI reconstruction, we also propose several new tasks for 3D HOI understanding to pave the way for future work. Data and code will be publicly available at https://wenboran2002.github.io/3dhoi. |
| title | Reconstructing In-the-Wild Open-Vocabulary Human-Object Interactions |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.15898 |