Hi3DGen：通過法線橋接實現從圖像生成高保真三維幾何

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

March 28, 2025

作者: Chongjie Ye, Yushuang Wu, Ziteng Lu, Jiahao Chang, Xiaoyang Guo, Jiaqing Zhou, Hao Zhao, Xiaoguang Han

cs.AI

摘要

隨著從二維圖像生成高保真三維模型的需求日益增長，現有方法在精確再現細粒度幾何細節方面仍面臨重大挑戰，這主要受限於領域差距和RGB圖像固有的模糊性。為解決這些問題，我們提出了Hi3DGen，這是一個通過法線橋接從圖像生成高保真三維幾何的新框架。Hi3DGen包含三個關鍵組件：(1) 一個圖像到法線的估計器，它通過噪聲注入和雙流訓練解耦低高頻圖像模式，以實現可泛化、穩定且銳利的估計；(2) 一種法線到幾何的學習方法，利用法線正則化的潛在擴散學習來提升三維幾何生成的保真度；(3) 一個三維數據合成管道，構建高質量數據集以支持訓練。大量實驗證明了我們框架在生成豐富幾何細節方面的有效性和優越性，在保真度上超越了現有最先進的方法。我們的工作通過利用法線圖作為中間表示，為從圖像生成高保真三維幾何提供了新的方向。

English

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.