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Wonder3D: How We Distributed the 3D Assets

Written by @ringi | Published on 2025/1/1

TL;DR
We propose that the distribution of 3d assets can be modeled as a joint distribution of its corresponding 2d multi-view normal maps and corresponding images.

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

3.2. The Distribution of 3D Assets

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.

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Written by
@ringi
Unleashing the power of collective insight, where diverse perspectives converge to shape most impactful decisions.

Topics and
tags
3d-assets|multi-view-diffusion-models|wonder3d|wonder3d-explained|what-is-wonder3d|3d-geometry|markov-chain|gaussian-noises
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