TexGen: Text-Guided 3D Texture Generation with Multi-view Sampling and Resampling
Dong Huo, Zixin Guo, Xinxin Zuo, Zhihao Shi, Juwei Lu, Peng Dai,, Songcen Xu, Li Cheng, Yee-Hong Yang

TL;DR
TexGen introduces a multi-view sampling and resampling framework leveraging a pre-trained text-to-image diffusion model to generate high-quality, view-consistent 3D textures from textual descriptions, outperforming existing methods.
Contribution
The paper presents a novel multi-view sampling and noise resampling framework for 3D texture generation that enhances view consistency and texture detail preservation using diffusion models.
Findings
Produces significantly better texture quality than state-of-the-art methods.
Ensures high view consistency and rich appearance details.
Applicable to texture editing while maintaining original identity.
Abstract
Given a 3D mesh, we aim to synthesize 3D textures that correspond to arbitrary textual descriptions. Current methods for generating and assembling textures from sampled views often result in prominent seams or excessive smoothing. To tackle these issues, we present TexGen, a novel multi-view sampling and resampling framework for texture generation leveraging a pre-trained text-to-image diffusion model. For view consistent sampling, first of all we maintain a texture map in RGB space that is parameterized by the denoising step and updated after each sampling step of the diffusion model to progressively reduce the view discrepancy. An attention-guided multi-view sampling strategy is exploited to broadcast the appearance information across views. To preserve texture details, we develop a noise resampling technique that aids in the estimation of noise, generating inputs for subsequent…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Image Processing and 3D Reconstruction · Computer Graphics and Visualization Techniques
MethodsDiffusion
