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Wonder3D方法，创新性地实现了从单视角图像高效生成高保真纹理网格。基于分数蒸馏采样（Score Distillation Sampling, SDS）的现有方法虽展现出从2D扩散先验恢复3D几何的潜力，但通常存在逐形状优化耗时且几何一致性不足的问题。相反，部分工作通过快速网络推理直接生成3D信息，但结果往往质量低下且缺乏几何细节。为全面提升图像到三维任务的质量、一致性和效率，我们提出一种跨域扩散模型，可生成多视角法向图及对应彩色图像。为确保一致性，我们采用多视角跨域注意力机制，促进不同视角与模态间的信息交互。最后，引入几何感知法向融合算法，从多视角2D表征中提取高质量曲面。大量评估表明，与现有方法相比，本方法在实现高质量重建结果、强鲁棒泛化能力的同时，亦保持了较优的效率。