Hi3DGen：通过法线桥接实现图像到高保真三维几何的生成

Hi3DGen: High-fidelity 3D Geometry Generation from Images via Normal Bridging

March 28, 2025

作者: Chongjie Ye, Yushuang Wu, Ziteng Lu, Jiahao Chang, Xiaoyang Guo, Jiaqing Zhou, Hao Zhao, Xiaoguang Han

cs.AI

摘要

随着从二维图像生成高保真三维模型的需求日益增长，现有方法在准确再现细粒度几何细节方面仍面临重大挑战，这主要源于领域差距的限制以及RGB图像固有的模糊性。为解决这些问题，我们提出了Hi3DGen，一种通过法线桥接从图像生成高保真三维几何的新颖框架。Hi3DGen包含三个关键组件：(1) 图像到法线估计器，通过噪声注入和双流训练解耦图像的高低频模式，实现可泛化、稳定且锐利的估计；(2) 法线到几何的学习方法，采用法线正则化的潜在扩散学习，提升三维几何生成的保真度；(3) 三维数据合成管道，构建高质量数据集以支持训练。大量实验验证了我们框架在生成丰富几何细节方面的有效性和优越性，在保真度上超越了现有最先进方法。通过利用法线图作为中间表示，我们的工作为从图像生成高保真三维几何提供了新的方向。

English

With the growing demand for high-fidelity 3D models from 2D images, existing methods still face significant challenges in accurately reproducing fine-grained geometric details due to limitations in domain gaps and inherent ambiguities in RGB images. To address these issues, we propose Hi3DGen, a novel framework for generating high-fidelity 3D geometry from images via normal bridging. Hi3DGen consists of three key components: (1) an image-to-normal estimator that decouples the low-high frequency image pattern with noise injection and dual-stream training to achieve generalizable, stable, and sharp estimation; (2) a normal-to-geometry learning approach that uses normal-regularized latent diffusion learning to enhance 3D geometry generation fidelity; and (3) a 3D data synthesis pipeline that constructs a high-quality dataset to support training. Extensive experiments demonstrate the effectiveness and superiority of our framework in generating rich geometric details, outperforming state-of-the-art methods in terms of fidelity. Our work provides a new direction for high-fidelity 3D geometry generation from images by leveraging normal maps as an intermediate representation.