Table of Links Abstract and 1 Introduction 2. Related Works 2.1. 2D Diffusion Models for 3D Generation 2.2. 3D Generative Models and 2.3. Multi-view Diffusion Models 3. Problem Formulation 3.1. Diffusion Models 3.2. The Distribution of 3D Assets 4. Method and 4.1. Consistent Multi-view Generation 4.2. Cross-Domain Diffusion 4.3. Textured Mesh Extraction 5. Experiments 5.1. Implementation Details 5.2. Baselines 5.3. Evaluation Protocol 5.4. Single View Reconstruction 5.5. Novel View Synthesis and 5.6. Discussions 6. Conclusions and Future Works, Acknowledgements and References 5.2. Baselines We adopt Zero123 [31], RealFusion [38], Magic123 [44], One-2-3-45 [30], Point-E [41], Shap-E [25] and a recent work SyncDreamer [33] as baseline methods. Given an input image, zero123 is capable of generating novel views of arbitrary viewpoints, and it can be incorporated with SDS loss [43] for 3D reconstruction (we adopt the implementation of ThreeStudio [20]). RealFusion [38] and Magic123 [44] leverage Stable Diffusion [47] and SDS loss for single-view reconstruction. One-2-3-45 [30] directly predict SDFs via SparseNeuS [36] by taking the generated multiple images of Zero123 [31]. Point-E [41] and ShapE [25] are 3D generative models trained on a large internal OpenAI 3D dataset, both of which are able to convert a single-view image into a point cloud or an implicit representation. SyncDreamer[33] aims to generate multi-view consistent images from a single image for deriving 3D geometry. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors:
(1) Xiaoxiao Long, The University of Hong Kong, VAST, MPI Informatik and Equal Contributions;
(2) Yuan-Chen Guo, Tsinghua University, VAST and Equal Contributions;
(3) Cheng Lin, The University of Hong Kong with Corresponding authors;
(4) Yuan Liu, The University of Hong Kong;
(5) Zhiyang Dou, The University of Hong Kong;
(6) Lingjie Liu, University of Pennsylvania;
(7) Yuexin Ma, Shanghai Tech University;
(8) Song-Hai Zhang, The University of Hong Kong;
(9) Marc Habermann, MPI Informatik;
(10) Christian Theobalt, MPI Informatik;
(11) Wenping Wang, Texas A&M University with Corresponding authors. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2. Related Works 2.1. 2D Diffusion Models for 3D Generation 2.1. 2D Diffusion Models for 3D Generation 2.2. 3D Generative Models and 2.3. Multi-view Diffusion Models 2.2. 3D Generative Models and 2.3. Multi-view Diffusion Models 3. Problem Formulation 3.1. Diffusion Models 3.1. Diffusion Models 3.2. The Distribution of 3D Assets 3.2. The Distribution of 3D Assets 4. Method and 4.1. Consistent Multi-view Generation 4. Method and 4.1. Consistent Multi-view Generation 4.2. Cross-Domain Diffusion 4.2. Cross-Domain Diffusion 4.3. Textured Mesh Extraction 4.3. Textured Mesh Extraction 5. Experiments 5.1. Implementation Details 5.1. Implementation Details 5.2. Baselines 5.2. Baselines 5.3. Evaluation Protocol 5.3. Evaluation Protocol 5.4. Single View Reconstruction 5.4. Single View Reconstruction 5.5. Novel View Synthesis and 5.6. Discussions 5.5. Novel View Synthesis and 5.6. Discussions 6. Conclusions and Future Works, Acknowledgements and References 6. Conclusions and Future Works, Acknowledgements and References 5.2. Baselines We adopt Zero123 [31], RealFusion [38], Magic123 [44], One-2-3-45 [30], Point-E [41], Shap-E [25] and a recent work SyncDreamer [33] as baseline methods. Given an input image, zero123 is capable of generating novel views of arbitrary viewpoints, and it can be incorporated with SDS loss [43] for 3D reconstruction (we adopt the implementation of ThreeStudio [20]). RealFusion [38] and Magic123 [44] leverage Stable Diffusion [47] and SDS loss for single-view reconstruction. One-2-3-45 [30] directly predict SDFs via SparseNeuS [36] by taking the generated multiple images of Zero123 [31]. Point-E [41] and ShapE [25] are 3D generative models trained on a large internal OpenAI 3D dataset, both of which are able to convert a single-view image into a point cloud or an implicit representation. SyncDreamer[33] aims to generate multi-view consistent images from a single image for deriving 3D geometry. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv Authors: (1) Xiaoxiao Long, The University of Hong Kong, VAST, MPI Informatik and Equal Contributions; (2) Yuan-Chen Guo, Tsinghua University, VAST and Equal Contributions; (3) Cheng Lin, The University of Hong Kong with Corresponding authors; (4) Yuan Liu, The University of Hong Kong; (5) Zhiyang Dou, The University of Hong Kong; (6) Lingjie Liu, University of Pennsylvania; (7) Yuexin Ma, Shanghai Tech University; (8) Song-Hai Zhang, The University of Hong Kong; (9) Marc Habermann, MPI Informatik; (10) Christian Theobalt, MPI Informatik; (11) Wenping Wang, Texas A&M University with Corresponding authors. Authors: Authors: (1) Xiaoxiao Long, The University of Hong Kong, VAST, MPI Informatik and Equal Contributions; (2) Yuan-Chen Guo, Tsinghua University, VAST and Equal Contributions; (3) Cheng Lin, The University of Hong Kong with Corresponding authors; (4) Yuan Liu, The University of Hong Kong; (5) Zhiyang Dou, The University of Hong Kong; (6) Lingjie Liu, University of Pennsylvania; (7) Yuexin Ma, Shanghai Tech University; (8) Song-Hai Zhang, The University of Hong Kong; (9) Marc Habermann, MPI Informatik; (10) Christian Theobalt, MPI Informatik; (11) Wenping Wang, Texas A&M University with Corresponding authors.

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