Physically Plausible Human-Object Rendering from Sparse Views via 3D Gaussian Splatting
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918435982671872 |
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| author | Wang, Weiquan Xiao, Jun Yang, Yi Zhuang, Yueting Chen, Long |
| author_facet | Wang, Weiquan Xiao, Jun Yang, Yi Zhuang, Yueting Chen, Long |
| contents | Rendering realistic human-object interactions (HOIs) from sparse-view inputs is a challenging yet crucial task for various real-world applications. Existing methods often struggle to simultaneously achieve high rendering quality, physical plausibility, and computational efficiency. To address these limitations, we propose HOGS (Human-Object Rendering via 3D Gaussian Splatting), a novel framework for efficient HOI rendering with physically plausible geometric constraints from sparse views. HOGS represents both humans and objects as dynamic 3D Gaussians. Central to HOGS is a novel optimization process that operates directly on these Gaussians to enforce geometric consistency (i.e., preventing inter-penetration or floating contacts) to achieve physical plausibility. To support this core optimization under sparse-view ambiguity, our framework incorporates two pre-trained modules: an optimization-guided Human Pose Refiner for robust estimation under sparse-view occlusions, and a Human-Object Contact Predictor that efficiently identifies interaction regions to guide our novel contact and separation losses. Extensive experiments on both human-object and hand-object interaction datasets demonstrate that HOGS achieves state-of-the-art rendering quality and maintains high computational efficiency. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_09640 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physically Plausible Human-Object Rendering from Sparse Views via 3D Gaussian Splatting Wang, Weiquan Xiao, Jun Yang, Yi Zhuang, Yueting Chen, Long Graphics Computer Vision and Pattern Recognition Rendering realistic human-object interactions (HOIs) from sparse-view inputs is a challenging yet crucial task for various real-world applications. Existing methods often struggle to simultaneously achieve high rendering quality, physical plausibility, and computational efficiency. To address these limitations, we propose HOGS (Human-Object Rendering via 3D Gaussian Splatting), a novel framework for efficient HOI rendering with physically plausible geometric constraints from sparse views. HOGS represents both humans and objects as dynamic 3D Gaussians. Central to HOGS is a novel optimization process that operates directly on these Gaussians to enforce geometric consistency (i.e., preventing inter-penetration or floating contacts) to achieve physical plausibility. To support this core optimization under sparse-view ambiguity, our framework incorporates two pre-trained modules: an optimization-guided Human Pose Refiner for robust estimation under sparse-view occlusions, and a Human-Object Contact Predictor that efficiently identifies interaction regions to guide our novel contact and separation losses. Extensive experiments on both human-object and hand-object interaction datasets demonstrate that HOGS achieves state-of-the-art rendering quality and maintains high computational efficiency. |
| title | Physically Plausible Human-Object Rendering from Sparse Views via 3D Gaussian Splatting |
| topic | Graphics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.09640 |