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