  Recent text-to-3D methods employing diffusion models have made significant
advancements in 3D human generation. However, these approaches face challenges
due to the limitations of text-to-image diffusion models, which lack an
understanding of 3D structures. Consequently, these methods struggle to achieve
high-quality human generation, resulting in smooth geometry and cartoon-like
appearances. In this paper, we propose HumanNorm, a novel approach for
high-quality and realistic 3D human generation. The main idea is to enhance the
model's 2D perception of 3D geometry by learning a normal-adapted diffusion
model and a normal-aligned diffusion model. The normal-adapted diffusion model
can generate high-fidelity normal maps corresponding to user prompts with
view-dependent and body-aware text. The normal-aligned diffusion model learns
to generate color images aligned with the normal maps, thereby transforming
physical geometry details into realistic appearance. Leveraging the proposed
normal diffusion model, we devise a progressive geometry generation strategy
and a multi-step Score Distillation Sampling (SDS) loss to enhance the
performance of 3D human generation. Comprehensive experiments substantiate
HumanNorm's ability to generate 3D humans with intricate geometry and realistic
appearances. HumanNorm outperforms existing text-to-3D methods in both geometry
and texture quality. The project page of HumanNorm is
https://humannorm.github.io/.
