Gaussian Control with Hierarchical Semantic Graphs in 3D Human Recovery

• URL: http://arxiv.org/abs/2405.12477v3
• Date: Sat, 22 Jun 2024 02:07:40 GMT
• Title: Gaussian Control with Hierarchical Semantic Graphs in 3D Human Recovery
• Authors: Hongsheng Wang, Weiyue Zhang, Sihao Liu, Xinrui Zhou, Jing Li, Zhanyun Tang, Shengyu Zhang, Fei Wu, Feng Lin,
• Abstract summary: 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. Our method exhibits superior performance in human body reconstruction, particularly in enhancing surface details and accurately reconstructing body part junctions.
• Score: 15.58274601909995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/.

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