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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2405.12477 |
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| _version_ | 1866911929325322240 |
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| author | Wang, Hongsheng Zhang, Weiyue Liu, Sihao Zhou, Xinrui Li, Jing Tang, Zhanyun Zhang, Shengyu Wu, Fei Lin, Feng |
| author_facet | Wang, Hongsheng Zhang, Weiyue Liu, Sihao Zhou, Xinrui Li, Jing Tang, Zhanyun Zhang, Shengyu Wu, Fei Lin, Feng |
| contents | Although 3D Gaussian Splatting (3DGS) has recently made progress in 3D human reconstruction, it primarily relies on 2D pixel-level supervision, overlooking the geometric complexity and topological relationships of different body parts. To address this gap, we introduce the Hierarchical Graph Human Gaussian Control (HUGS) framework for achieving high-fidelity 3D human reconstruction. Our approach involves leveraging explicitly semantic priors of body parts to ensure the consistency of geometric topology, thereby enabling the capture of the complex geometrical and topological associations among body parts. Additionally, we disentangle high-frequency features from global human features to refine surface details in body parts. Extensive experiments demonstrate that our method exhibits superior performance in human body reconstruction, particularly in enhancing surface details and accurately reconstructing body part junctions. Codes are available at https://wanghongsheng01.github.io/HUGS/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_12477 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Gaussian Control with Hierarchical Semantic Graphs in 3D Human Recovery Wang, Hongsheng Zhang, Weiyue Liu, Sihao Zhou, Xinrui Li, Jing Tang, Zhanyun Zhang, Shengyu Wu, Fei Lin, Feng Computer Vision and Pattern Recognition Although 3D Gaussian Splatting (3DGS) has recently made progress in 3D human reconstruction, it primarily relies on 2D pixel-level supervision, overlooking the geometric complexity and topological relationships of different body parts. To address this gap, we introduce the Hierarchical Graph Human Gaussian Control (HUGS) framework for achieving high-fidelity 3D human reconstruction. Our approach involves leveraging explicitly semantic priors of body parts to ensure the consistency of geometric topology, thereby enabling the capture of the complex geometrical and topological associations among body parts. Additionally, we disentangle high-frequency features from global human features to refine surface details in body parts. Extensive experiments demonstrate that our method exhibits superior performance in human body reconstruction, particularly in enhancing surface details and accurately reconstructing body part junctions. Codes are available at https://wanghongsheng01.github.io/HUGS/. |
| title | Gaussian Control with Hierarchical Semantic Graphs in 3D Human Recovery |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.12477 |