Hi3DGen: High-fidelity 3D Geometry Generation from Images via Normal
  Bridging
Authors: name: Chongjie Ye, name: Yushuang Wu, name: Ziteng Lu, name: Jiahao Chang, name: Xiaoyang Guo, name: Jiaqing Zhou, name: Hao Zhao, name: Xiaoguang Han

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.