HumanNorm: Learning Normal Diffusion Model for High-quality and Realistic 3D Human Generation

TL;DR

HumanNorm introduces a novel diffusion-based approach that significantly improves the quality and realism of 3D human generation by learning normal and color alignment models, surpassing existing methods.

Contribution

The paper proposes a new normal-adapted and normal-aligned diffusion model to enhance 3D human generation quality and realism, addressing limitations of previous text-to-3D methods.

Findings

Outperforms existing text-to-3D methods in geometry quality

Produces more realistic textures and detailed geometry

Demonstrates effective progressive geometry generation

Abstract

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/.