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

• URL: http://arxiv.org/abs/2503.22236v2
• Date: Mon, 31 Mar 2025 03:41:01 GMT
• Title: Hi3DGen: High-fidelity 3D Geometry Generation from Images via Normal Bridging
• Authors: Chongjie Ye, Yushuang Wu, Ziteng Lu, Jiahao Chang, Xiaoyang Guo, Jiaqing Zhou, Hao Zhao, Xiaoguang Han,
• Abstract summary: Hi3DGen is a novel framework for generating high-fidelity 3D geometry from images via normal bridging.<n>Our work provides a new direction for high-fidelity 3D geometry generation from images by leveraging normal maps as an intermediate representation.
• Score: 15.36983068580743
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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