Wonder3D: Single Image to 3D using Cross-Domain Diffusion
Authors: name: Xiaoxiao Long, name: Yuan-Chen Guo, name: Cheng Lin, name: Yuan Liu, name: Zhiyang Dou, name: Lingjie Liu, name: Yuexin Ma, name: Song-Hai Zhang, name: Marc Habermann, name: Christian Theobalt, name: Wenping Wang

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.