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Related papers: TexGen: Text-Guided 3D Texture Generation with Mul…

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We present TexFusion (Texture Diffusion), a new method to synthesize textures for given 3D geometries, using large-scale text-guided image diffusion models. In contrast to recent works that leverage 2D text-to-image diffusion models to…

Computer Vision and Pattern Recognition · Computer Science 2023-10-24 Tianshi Cao , Karsten Kreis , Sanja Fidler , Nicholas Sharp , Kangxue Yin

We present Text2Tex, a novel method for generating high-quality textures for 3D meshes from the given text prompts. Our method incorporates inpainting into a pre-trained depth-aware image diffusion model to progressively synthesize high…

Computer Vision and Pattern Recognition · Computer Science 2023-03-22 Dave Zhenyu Chen , Yawar Siddiqui , Hsin-Ying Lee , Sergey Tulyakov , Matthias Nießner

While high-quality texture maps are essential for realistic 3D asset rendering, few studies have explored learning directly in the texture space, especially on large-scale datasets. In this work, we depart from the conventional approach of…

Computer Vision and Pattern Recognition · Computer Science 2024-11-25 Xin Yu , Ze Yuan , Yuan-Chen Guo , Ying-Tian Liu , JianHui Liu , Yangguang Li , Yan-Pei Cao , Ding Liang , Xiaojuan Qi

The recent success of pre-trained diffusion models unlocks the possibility of the automatic generation of textures for arbitrary 3D meshes in the wild. However, these models are trained in the screen space, while converting them to a…

Computer Vision and Pattern Recognition · Computer Science 2024-06-28 Hongkun Zhang , Zherong Pan , Congyi Zhang , Lifeng Zhu , Xifeng Gao

This paper introduces a novel approach to synthesize texture to dress up a given 3D object, given a text prompt. Based on the pretrained text-to-image (T2I) diffusion model, existing methods usually employ a project-and-inpaint approach, in…

Computer Vision and Pattern Recognition · Computer Science 2025-03-19 Yuxin Liu , Minshan Xie , Hanyuan Liu , Tien-Tsin Wong

We present GenesisTex, a novel method for synthesizing textures for 3D geometries from text descriptions. GenesisTex adapts the pretrained image diffusion model to texture space by texture space sampling. Specifically, we maintain a latent…

Computer Vision and Pattern Recognition · Computer Science 2024-03-27 Chenjian Gao , Boyan Jiang , Xinghui Li , Yingpeng Zhang , Qian Yu

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…

Graphics · Computer Science 2025-05-09 Zilong Chen , Yikai Wang , Wenqiang Sun , Feng Wang , Yiwen Chen , Huaping Liu

The recent availability and adaptability of text-to-image models has sparked a new era in many related domains that benefit from the learned text priors as well as high-quality and fast generation capabilities, one of which is texture…

Computer Vision and Pattern Recognition · Computer Science 2024-07-03 Raphael Bensadoun , Yanir Kleiman , Idan Azuri , Omri Harosh , Andrea Vedaldi , Natalia Neverova , Oran Gafni

Large-scale text-guided image diffusion models have shown astonishing results in text-to-image (T2I) generation. However, applying these models to synthesize textures for 3D geometries remains challenging due to the domain gap between 2D…

Computer Vision and Pattern Recognition · Computer Science 2024-09-30 Jiawei Lu , Yingpeng Zhang , Zengjun Zhao , He Wang , Kun Zhou , Tianjia Shao

Despite the latest remarkable advances in generative modeling, efficient generation of high-quality 3D assets from textual prompts remains a difficult task. A key challenge lies in data scarcity: the most extensive 3D datasets encompass…

Computer Vision and Pattern Recognition · Computer Science 2024-01-17 Antoine Mercier , Ramin Nakhli , Mahesh Reddy , Rajeev Yasarla , Hong Cai , Fatih Porikli , Guillaume Berger

In this paper, we present TEXTure, a novel method for text-guided generation, editing, and transfer of textures for 3D shapes. Leveraging a pretrained depth-to-image diffusion model, TEXTure applies an iterative scheme that paints a 3D…

Computer Vision and Pattern Recognition · Computer Science 2023-02-06 Elad Richardson , Gal Metzer , Yuval Alaluf , Raja Giryes , Daniel Cohen-Or

Style-guided texture generation aims to generate a texture that is harmonious with both the style of the reference image and the geometry of the input mesh, given a reference style image and a 3D mesh with its text description. Although…

Computer Vision and Pattern Recognition · Computer Science 2024-11-04 Zhiyu Xie , Yuqing Zhang , Xiangjun Tang , Yiqian Wu , Dehan Chen , Gongsheng Li , Xaogang Jin

Text-to-texture generation has recently attracted increasing attention, but existing methods often suffer from the problems of view inconsistencies, apparent seams, and misalignment between textures and the underlying mesh. In this paper,…

Computer Vision and Pattern Recognition · Computer Science 2024-10-01 Jangyeong Kim , Donggoo Kang , Junyoung Choi , Jeonga Wi , Junho Gwon , Jiun Bae , Dumim Yoon , Junghyun Han

We present TexTailor, a novel method for generating consistent object textures from textual descriptions. Existing text-to-texture synthesis approaches utilize depth-aware diffusion models to progressively generate images and synthesize…

Computer Vision and Pattern Recognition · Computer Science 2025-06-13 Suin Lee , Dae-Shik Kim

This paper presents a method to reconstruct high-quality textured 3D models from both multi-view and single-view images. The reconstruction is posed as an adaptation problem and is done progressively where in the first stage, we focus on…

Computer Vision and Pattern Recognition · Computer Science 2022-03-18 Aysegul Dundar , Jun Gao , Andrew Tao , Bryan Catanzaro

Texture map production is an important part of 3D modeling and determines the rendering quality. Recently, diffusion-based methods have opened a new way for texture generation. However, restricted control flexibility and limited prompt…

Graphics · Computer Science 2025-06-04 Dongyu Yan , Leyi Wu , Jiantao Lin , Luozhou Wang , Tianshuo Xu , Zhifei Chen , Zhen Yang , Lie Xu , Shunsi Zhang , Yingcong Chen

High-quality 3D texture generation remains a fundamental challenge due to the view-inconsistency inherent in current mainstream multi-view diffusion pipelines. Existing representations either rely on UV maps, which suffer from distortion…

Computer Vision and Pattern Recognition · Computer Science 2026-02-17 Ziteng Lu , Yushuang Wu , Chongjie Ye , Yuda Qiu , Jing Shao , Xiaoyang Guo , Jiaqing Zhou , Tianlei Hu , Kun Zhou , Xiaoguang Han

Given a 3D mesh with a UV parameterization, we introduce a novel approach to generating textures from text prompts. While prior work uses optimization from Text-to-Image Diffusion models to generate textures and geometry, this is slow and…

Computer Vision and Pattern Recognition · Computer Science 2023-12-05 Julian Knodt , Xifeng Gao

Recent advances in 3D scene generation produce visually appealing output, but current representations hinder artists' workflows that require modifiable 3D textured mesh scenes for visual effects and game development. Despite significant…

Computer Vision and Pattern Recognition · Computer Science 2025-12-22 Tobias Sautter , Jan-Niklas Dihlmann , Hendrik P. A. Lensch

Text- or image-to-3D generators and 3D scanners can now produce 3D assets with high-quality shapes and textures. These assets typically consist of a single, fused representation, like an implicit neural field, a Gaussian mixture, or a mesh,…

Computer Vision and Pattern Recognition · Computer Science 2024-12-31 Minghao Chen , Roman Shapovalov , Iro Laina , Tom Monnier , Jianyuan Wang , David Novotny , Andrea Vedaldi
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