In this work, we introduce Wonder3D, a novel method for efficiently generating high-fidelity textured meshes from single-view images.Recent methods based on Score Distillation Sampling (SDS) have shown the potential to recover 3D geometry from 2D diffusion priors, but they typically suffer from time-consuming per-shape optimization and inconsistent geometry. In contrast, certain works directly produce 3D information via fast network inferences, but their results are often of low quality and lack geometric details. To holistically improve the quality, consistency, and efficiency of image-to-3D tasks, we propose a cross-domain diffusion model that generates multi-view normal maps and the corresponding color images. To ensure consistency, we employ a multi-view cross-domain attention mechanism that facilitates information exchange across views and modalities. Lastly, we introduce a geometry-aware normal fusion algorithm that extracts high-quality surfaces from the multi-view 2D representations. Our extensive evaluations demonstrate that our method achieves high-quality reconstruction results, robust generalization, and reasonably good efficiency compared to prior works.

翻译：本文提出了Wonder3D，一种从单视角图像高效生成高保真纹理网格的新方法。基于分数蒸馏采样的近期方法已展现出从二维扩散先验恢复三维几何的潜力，但通常面临逐形状优化耗时及几何不一致的问题。相比之下，部分工作通过快速网络推理直接生成三维信息，但其结果往往质量较低且缺乏几何细节。为整体提升图像到三维任务的质量、一致性与效率，我们提出了一种跨域扩散模型，可生成多视角法向图及其对应的彩色图像。为保障一致性，我们采用多视角跨域注意力机制，促进跨视角与跨模态的信息交互。最后，我们引入几何感知法向融合算法，从多视角二维表示中提取高质量表面。大量评估表明，与先前工作相比，我们的方法实现了高质量重建结果、鲁棒的泛化能力以及较为理想的效率。