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Related papers: Text2Tex: Text-driven Texture Synthesis via Diffus…

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We present Make-A-Texture, a new framework that efficiently synthesizes high-resolution texture maps from textual prompts for given 3D geometries. Our approach progressively generates textures that are consistent across multiple viewpoints…

Computer Vision and Pattern Recognition · Computer Science 2025-01-28 Xiaoyu Xiang , Liat Sless Gorelik , Yuchen Fan , Omri Armstrong , Forrest Iandola , Yilei Li , Ita Lifshitz , Rakesh Ranjan

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

Fabricating and designing 3D garments has become extremely demanding with the increasing need for synthesizing realistic dressed persons for a variety of applications, e.g. 3D virtual try-on, digitalization of 2D clothes into 3D apparel,…

Computer Vision and Pattern Recognition · Computer Science 2023-08-09 Daiheng Gao , Xu Chen , Xindi Zhang , Qi Wang , Ke Sun , Bang Zhang , Liefeng Bo , Qixing Huang

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

Text-to-texture synthesis has become a new frontier in 3D content creation thanks to the recent advances in text-to-image models. Existing methods primarily adopt a combination of pretrained depth-aware diffusion and inpainting models, yet…

Computer Vision and Pattern Recognition · Computer Science 2024-03-19 Jiaxiang Tang , Ruijie Lu , Xiaokang Chen , Xiang Wen , Gang Zeng , Ziwei Liu

This paper addresses the problem of generating textures for 3D mesh assets. Existing approaches often rely on image diffusion models to generate multi-view image observations, which are then transformed onto the mesh surface to produce a…

Computer Vision and Pattern Recognition · Computer Science 2025-04-02 Xuyang Wang , Ziang Cheng , Zhenyu Li , Jiayu Yang , Haorui Ji , Pan Ji , Mehrtash Harandi , Richard Hartley , Hongdong Li

We introduce MD-ProjTex, a method for fast and consistent text-guided texture generation for 3D shapes using pretrained text-to-image diffusion models. At the core of our approach is a multi-view consistency mechanism in UV space, which…

Computer Vision and Pattern Recognition · Computer Science 2025-04-04 Ahmet Burak Yildirim , Mustafa Utku Aydogdu , Duygu Ceylan , Aysegul Dundar

We introduce RealmDreamer, a technique for generating forward-facing 3D scenes from text descriptions. Our method optimizes a 3D Gaussian Splatting representation to match complex text prompts using pretrained diffusion models. Our key…

Computer Vision and Pattern Recognition · Computer Science 2025-03-12 Jaidev Shriram , Alex Trevithick , Lingjie Liu , Ravi Ramamoorthi

We present Im2SurfTex, a method that generates textures for input 3D shapes by learning to aggregate multi-view image outputs produced by 2D image diffusion models onto the shapes' texture space. Unlike existing texture generation…

Graphics · Computer Science 2025-12-11 Yiangos Georgiou , Marios Loizou , Melinos Averkiou , Evangelos Kalogerakis

Recent advances in generative modeling have driven significant progress in text-guided texture synthesis. However, current methods focus on synthesizing texture for single static 3D object, and struggle to handle entire families of shapes,…

Graphics · Computer Science 2025-10-07 Ruiqi Xu , Zihan Zhu , Ben Ahlbrand , Srinath Sridhar , Daniel Ritchie

We introduce a framework for intrinsic latent diffusion models operating directly on the surfaces of 3D shapes, with the goal of synthesizing high-quality textures. Our approach is underpinned by two contributions: field latents, a latent…

Computer Vision and Pattern Recognition · Computer Science 2024-05-30 Thomas W. Mitchel , Carlos Esteves , Ameesh Makadia

Text-driven 3D scene generation is widely applicable to video gaming, film industry, and metaverse applications that have a large demand for 3D scenes. However, existing text-to-3D generation methods are limited to producing 3D objects with…

Computer Vision and Pattern Recognition · Computer Science 2024-02-01 Jingbo Zhang , Xiaoyu Li , Ziyu Wan , Can Wang , Jing Liao

Generating 3D CT volumes from descriptive free-text inputs presents a transformative opportunity in diagnostics and research. In this paper, we introduce Text2CT, a novel approach for synthesizing 3D CT volumes from textual descriptions…

Image and Video Processing · Electrical Eng. & Systems 2025-05-09 Pengfei Guo , Can Zhao , Dong Yang , Yufan He , Vishwesh Nath , Ziyue Xu , Pedro R. A. S. Bassi , Zongwei Zhou , Benjamin D. Simon , Stephanie Anne Harmon , Baris Turkbey , Daguang Xu

Current texture synthesis methods, which generate textures from fixed viewpoints, suffer from inconsistencies due to the lack of global context and geometric understanding. Meanwhile, recent advancements in video generation models have…

Graphics · Computer Science 2025-06-27 Donggoo Kang , Jangyeong Kim , Dasol Jeong , Junyoung Choi , Jeonga Wi , Hyunmin Lee , Joonho Gwon , Joonki Paik

Textual image generation spans diverse fields like advertising, education, product packaging, social media, information visualization, and branding. Despite recent strides in language-guided image synthesis using diffusion models, current…

Computer Vision and Pattern Recognition · Computer Science 2024-05-22 Shubham Paliwal , Arushi Jain , Monika Sharma , Vikram Jamwal , Lovekesh Vig

We propose Text2Scene, a method to automatically create realistic textures for virtual scenes composed of multiple objects. Guided by a reference image and text descriptions, our pipeline adds detailed texture on labeled 3D geometries in…

Computer Vision and Pattern Recognition · Computer Science 2023-09-01 Inwoo Hwang , Hyeonwoo Kim , Young Min Kim

Generating high-quality textures for 3D assets is a challenging task. Existing multiview texture generation methods suffer from the multiview inconsistency and missing textures on unseen parts, while UV inpainting texture methods do not…

Computer Vision and Pattern Recognition · Computer Science 2026-03-17 Zheng Zhang , Qinchuan Zhang , Yuteng Ye , Zhi Chen , Penglei Ji , Mengfei Li , Wenxiao Zhang , Yuan Liu

In this work, we focus on synthesizing high-quality textures on 3D meshes. We present Point-UV diffusion, a coarse-to-fine pipeline that marries the denoising diffusion model with UV mapping to generate 3D consistent and high-quality…

Computer Vision and Pattern Recognition · Computer Science 2023-08-22 Xin Yu , Peng Dai , Wenbo Li , Lan Ma , Zhengzhe Liu , Xiaojuan Qi

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

While diffusion models have significantly advanced the quality of image generation their capability to accurately and coherently render text within these images remains a substantial challenge. Conventional diffusion-based methods for scene…

Computer Vision and Pattern Recognition · Computer Science 2024-09-17 Qilong Zhangli , Jindong Jiang , Di Liu , Licheng Yu , Xiaoliang Dai , Ankit Ramchandani , Guan Pang , Dimitris N. Metaxas , Praveen Krishnan