  The field of 3D detailed human mesh reconstruction has made significant
progress in recent years. However, current methods still face challenges when
used in industrial applications due to unstable results, low-quality meshes,
and a lack of UV unwrapping and skinning weights. In this paper, we present
SHERT, a novel pipeline that can reconstruct semantic human meshes with
textures and high-precision details. SHERT applies semantic- and normal-based
sampling between the detailed surface (e.g. mesh and SDF) and the corresponding
SMPL-X model to obtain a partially sampled semantic mesh and then generates the
complete semantic mesh by our specifically designed self-supervised completion
and refinement networks. Using the complete semantic mesh as a basis, we employ
a texture diffusion model to create human textures that are driven by both
images and texts. Our reconstructed meshes have stable UV unwrapping,
high-quality triangle meshes, and consistent semantic information. The given
SMPL-X model provides semantic information and shape priors, allowing SHERT to
perform well even with incorrect and incomplete inputs. The semantic
information also makes it easy to substitute and animate different body parts
such as the face, body, and hands. Quantitative and qualitative experiments
demonstrate that SHERT is capable of producing high-fidelity and robust
semantic meshes that outperform state-of-the-art methods.
