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Related papers: Guide3D: Create 3D Avatars from Text and Image Gui…

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Despite having tremendous progress in image-to-3D generation, existing methods still struggle to produce multi-view consistent images with high-resolution textures in detail, especially in the paradigm of 2D diffusion that lacks 3D…

Computer Vision and Pattern Recognition · Computer Science 2024-09-12 Haibo Yang , Yang Chen , Yingwei Pan , Ting Yao , Zhineng Chen , Chong-Wah Ngo , Tao Mei

High-fidelity 3D garment synthesis from text is desirable yet challenging for digital avatar creation. Recent diffusion-based approaches via Score Distillation Sampling (SDS) have enabled new possibilities but either intricately couple with…

Computer Vision and Pattern Recognition · Computer Science 2024-06-25 Yufei Liu , Junshu Tang , Chu Zheng , Shijie Zhang , Jinkun Hao , Junwei Zhu , Dongjin Huang

Current controls over diffusion models (e.g., through text or ControlNet) for image generation fall short in recognizing abstract, continuous attributes like illumination direction or non-rigid shape change. In this paper, we present an…

Computer Vision and Pattern Recognition · Computer Science 2024-02-14 Ta-Ying Cheng , Matheus Gadelha , Thibault Groueix , Matthew Fisher , Radomir Mech , Andrew Markham , Niki Trigoni

Text-to-3D generation has achieved significant success by incorporating powerful 2D diffusion models, but insufficient 3D prior knowledge also leads to the inconsistency of 3D geometry. Recently, since large-scale multi-view datasets have…

Computer Vision and Pattern Recognition · Computer Science 2024-05-03 Junyoung Seo , Susung Hong , Wooseok Jang , Inès Hyeonsu Kim , Minseop Kwak , Doyup Lee , Seungryong Kim

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 their ability to generate high-resolution and diverse images from text prompts, text-to-image diffusion models often suffer from slow iterative sampling processes. Model distillation is one of the most effective directions to…

Computer Vision and Pattern Recognition · Computer Science 2024-11-19 Thuan Hoang Nguyen , Anh Tran

Text-to-image generative models can generate high-quality humans, but realism is lost when generating hands. Common artifacts include irregular hand poses, shapes, incorrect numbers of fingers, and physically implausible finger…

Computer Vision and Pattern Recognition · Computer Science 2024-11-26 Supreeth Narasimhaswamy , Uttaran Bhattacharya , Xiang Chen , Ishita Dasgupta , Saayan Mitra , Minh Hoai

The evolution of 3D generative modeling has been notably propelled by the adoption of 2D diffusion models. Despite this progress, the cumbersome optimization process per se presents a critical hurdle to efficiency. In this paper, we…

Computer Vision and Pattern Recognition · Computer Science 2024-04-10 Xingyi Yang , Xinchao Wang

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

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

Recent advancements in diffusion techniques have propelled image and video generation to unprecedented levels of quality, significantly accelerating the deployment and application of generative AI. However, 3D shape generation technology…

Computer Vision and Pattern Recognition · Computer Science 2025-03-28 Yangguang Li , Zi-Xin Zou , Zexiang Liu , Dehu Wang , Yuan Liang , Zhipeng Yu , Xingchao Liu , Yuan-Chen Guo , Ding Liang , Wanli Ouyang , Yan-Pei Cao

Text-to-3D generation has recently garnered significant attention, fueled by 2D diffusion models trained on billions of image-text pairs. Existing methods primarily rely on score distillation to leverage the 2D diffusion priors to supervise…

Computer Vision and Pattern Recognition · Computer Science 2023-07-27 Chaohui Yu , Qiang Zhou , Jingliang Li , Zhe Zhang , Zhibin Wang , Fan Wang

Generating high-fidelity upper-body 3D avatars from one-shot input image remains a significant challenge. Current 3D avatar generation methods, which rely on large reconstruction models, are fast and capable of producing stable body…

Computer Vision and Pattern Recognition · Computer Science 2025-12-10 Fan Yang , Heyuan Li , Peihao Li , Weihao Yuan , Lingteng Qiu , Chaoyue Song , Cheng Chen , Yisheng He , Shifeng Zhang , Xiaoguang Han , Steven Hoi , Guosheng Lin

In this work, we explore the challenging task of generating 3D shapes from text. Beyond the existing works, we propose a new approach for text-guided 3D shape generation, capable of producing high-fidelity shapes with colors that match the…

Computer Vision and Pattern Recognition · Computer Science 2022-03-29 Zhengzhe Liu , Yi Wang , Xiaojuan Qi , Chi-Wing Fu

Diffusion models have achieved great success in generating 2D images. However, the quality and generalizability of 3D content generation remain limited. State-of-the-art methods often require large-scale 3D assets for training, which are…

Graphics · Computer Science 2025-03-24 Jiantao Lin , Xin Yang , Meixi Chen , Yingjie Xu , Dongyu Yan , Leyi Wu , Xinli Xu , Lie XU , Shunsi Zhang , Ying-Cong Chen

Score distillation sampling~(SDS) has been widely adopted to overcome the absence of unseen views in reconstructing 3D objects from a \textbf{single} image. It leverages pre-trained 2D diffusion models as teacher to guide the reconstruction…

Computer Vision and Pattern Recognition · Computer Science 2024-05-06 Xuanyu Yi , Zike Wu , Qingshan Xu , Pan Zhou , Joo-Hwee Lim , Hanwang Zhang

Video try-on replaces clothing in videos with target garments. Existing methods struggle to generate high-quality and temporally consistent results when handling complex clothing patterns and diverse body poses. We present 3DV-TON, a novel…

Computer Vision and Pattern Recognition · Computer Science 2025-04-25 Min Wei , Chaohui Yu , Jingkai Zhou , Fan Wang

As several industries are moving towards modeling massive 3D virtual worlds, the need for content creation tools that can scale in terms of the quantity, quality, and diversity of 3D content is becoming evident. In our work, we aim to train…

Computer Vision and Pattern Recognition · Computer Science 2022-09-23 Jun Gao , Tianchang Shen , Zian Wang , Wenzheng Chen , Kangxue Yin , Daiqing Li , Or Litany , Zan Gojcic , Sanja Fidler

Large-scale text-to-image diffusion models have significantly improved the state of the art in generative image modelling and allow for an intuitive and powerful user interface to drive the image generation process. Expressing spatial…

Computer Vision and Pattern Recognition · Computer Science 2023-06-27 Guillaume Couairon , Marlène Careil , Matthieu Cord , Stéphane Lathuilière , Jakob Verbeek

Creating 3D assets that follow the texture and geometry style of existing ones is often desirable or even inevitable in practical applications like video gaming and virtual reality. While impressive progress has been made in generating 3D…

Computer Vision and Pattern Recognition · Computer Science 2025-09-18 Zefan Qu , Zhenwei Wang , Haoyuan Wang , Ke Xu , Gerhard Hancke , Rynson W. H. Lau