  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/.
