title: Wonder3D: Single Image to 3D using Cross-Domain Diffusion
summary: 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.
paper_authors._id: 6538b453c8b0cccf1f3fe149
paper_authors.name: Xiaoxiao Long
paper_authors._id: 6538b453c8b0cccf1f3fe14a
paper_authors.name: Yuan-Chen Guo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-10-26T08:00:11.088Z
paper_authors._id: 6346aaa3f06b237ba4e297b0
paper_authors.avatarUrl: /avatars/5acb986e993eab1461200f3e9d99d022.svg
paper_authors.fullname: Yuan-Chen Guo
paper_authors.type: user
paper_authors.user: bennyguo
paper_authors._id: 6538b453c8b0cccf1f3fe14b
paper_authors.name: Cheng Lin
paper_authors._id: 6538b453c8b0cccf1f3fe14c
paper_authors.name: Yuan Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-10-26T08:01:34.741Z
paper_authors._id: 64d47a7a508a6313e33faedd
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paper_authors.fullname: Yuan Liu
paper_authors.type: user
paper_authors.user: YuanLiuuuuuu
paper_authors._id: 6538b453c8b0cccf1f3fe14d
paper_authors.name: Zhiyang Dou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-10-26T08:01:41.723Z
paper_authors._id: 645223fb01d7bd9555ea399a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/645223fb01d7bd9555ea399a/fVR7XmGg6pMSRKx9sEdvT.png
paper_authors.fullname: Zhiyang Dou
paper_authors.type: user
paper_authors.user: frankzydou
paper_authors._id: 6538b453c8b0cccf1f3fe14e
paper_authors.name: Lingjie Liu
paper_authors._id: 6538b453c8b0cccf1f3fe14f
paper_authors.name: Yuexin Ma
paper_authors._id: 6538b453c8b0cccf1f3fe150
paper_authors.name: Song-Hai Zhang
paper_authors._id: 6538b453c8b0cccf1f3fe151
paper_authors.name: Marc Habermann
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-29T08:07:05.449Z
paper_authors._id: 66d02ac970b7b22757b2c631
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/-71YM7txHBu67bfS_Re8-.jpeg
paper_authors.fullname: Marc Habermann
paper_authors.type: user
paper_authors.user: MarcHabermann
paper_authors._id: 6538b453c8b0cccf1f3fe152
paper_authors.name: Christian Theobalt
paper_authors._id: 6538b453c8b0cccf1f3fe153
paper_authors.name: Wenping Wang
paper_title: Wonder3D: Single Image to 3D using Cross-Domain Diffusion
paper_summary: 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.
paper_ai_summary: Wonder3D leverages cross-domain diffusion models and geometry-aware normal fusion for high-quality, efficient, and consistent 3D mesh generation from single-view images.
paper_ai_keywords: Score Distillation Sampling diffusion models multi-view normal maps cross-domain attention mechanism geometry-aware normal fusion 3D mesh generation