MeshGen: Generating PBR Textured Mesh with Render-Enhanced Auto-Encoder and Generative Data Augmentation
Zilong Chen, Yikai Wang, Wenqiang Sun, Feng Wang, Yiwen Chen, Huaping, Liu

TL;DR
MeshGen is a novel pipeline that generates high-quality 3D meshes with detailed geometry and PBR textures, overcoming limitations of existing models through innovative auto-encoder design and data augmentation techniques.
Contribution
It introduces a render-enhanced auto-encoder and generative augmentation methods to improve controllability, generalization, and texture consistency in 3D mesh generation.
Findings
Outperforms previous methods in shape quality
Achieves seamless and consistent PBR texturing
Enhances controllability with limited data
Abstract
In this paper, we introduce MeshGen, an advanced image-to-3D pipeline that generates high-quality 3D meshes with detailed geometry and physically based rendering (PBR) textures. Addressing the challenges faced by existing 3D native diffusion models, such as suboptimal auto-encoder performance, limited controllability, poor generalization, and inconsistent image-based PBR texturing, MeshGen employs several key innovations to overcome these limitations. We pioneer a render-enhanced point-to-shape auto-encoder that compresses meshes into a compact latent space by designing perceptual optimization with ray-based regularization. This ensures that the 3D shapes are accurately represented and reconstructed to preserve geometric details within the latent space. To address data scarcity and image-shape misalignment, we further propose geometric augmentation and generative rendering augmentation…
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Taxonomy
Topics3D Shape Modeling and Analysis · Generative Adversarial Networks and Image Synthesis · Computer Graphics and Visualization Techniques
