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

Chongjie Ye, Yushuang Wu, Ziteng Lu, Jiahao Chang, Xiaoyang Guo, Jiaqing Zhou, Hao Zhao, Xiaoguang Han; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025, pp. 25050-25061

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 highfidelity 3D geometry from images via normal bridging. Hi3DGen consists of three key components: 1. an imageto-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 normalregularized 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.