title: Hi3DGen: High-fidelity 3D Geometry Generation from Images via Normal
    Bridging
summary: 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.
paper_authors._id: 67e9fe132a2d5e305e4e6b80
paper_authors.name: Chongjie Ye
paper_authors._id: 67e9fe132a2d5e305e4e6b81
paper_authors.name: Yushuang Wu
paper_authors._id: 67e9fe132a2d5e305e4e6b82
paper_authors.name: Ziteng Lu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-31T08:31:42.733Z
paper_authors._id: 6735f447eb4b9c3f36dea354
paper_authors.avatarUrl: /avatars/396d007ba4d49ca62e604f3b5c227b42.svg
paper_authors.fullname: 鲁子腾
paper_authors.type: user
paper_authors.user: LUZITENG
paper_authors._id: 67e9fe132a2d5e305e4e6b83
paper_authors.name: Jiahao Chang
paper_authors._id: 67e9fe132a2d5e305e4e6b84
paper_authors.name: Xiaoyang Guo
paper_authors._id: 67e9fe132a2d5e305e4e6b85
paper_authors.name: Jiaqing Zhou
paper_authors._id: 67e9fe132a2d5e305e4e6b86
paper_authors.name: Hao Zhao
paper_authors._id: 67e9fe132a2d5e305e4e6b87
paper_authors.name: Xiaoguang Han
paper_title: Hi3DGen: High-fidelity 3D Geometry Generation from Images via Normal
    Bridging
paper_summary: 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.
paper_ai_summary: Hi3DGen generates high-fidelity 3D models from 2D images using normal maps and latent diffusion learning, achieving superior geometric detail.
paper_ai_keywords: image-to-normal estimator normal-to-geometry learning normal-regularized latent diffusion learning 3D geometry generation fidelity