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The field of text-to-image (T2I) generation has made significant progress in recent years, largely driven by advancements in diffusion models. Linguistic control enables effective content creation, but struggles with fine-grained control…

Computer Vision and Pattern Recognition · Computer Science 2024-07-19 Yanan Sun , Yanchen Liu , Yinhao Tang , Wenjie Pei , Kai Chen

In filmmaking, directors typically allow actors to perform freely based on the script before providing specific guidance on how to present key actions. AI-generated content faces similar requirements, where users not only need automatic…

Computer Vision and Pattern Recognition · Computer Science 2025-07-29 Zheng Qin , Ruobing Zheng , Yabing Wang , Tianqi Li , Zixin Zhu , Sanping Zhou , Ming Yang , Le Wang

Recent advances in diffusion models have significantly improved text-to-image (T2I) generation, but they often struggle to balance fine-grained precision with high-level control. Methods like ControlNet and T2I-Adapter excel at following…

Computer Vision and Pattern Recognition · Computer Science 2025-04-11 Pouyan Navard , Amin Karimi Monsefi , Mengxi Zhou , Wei-Lun Chao , Alper Yilmaz , Rajiv Ramnath

Text-to-image (T2I) generation has made remarkable progress in producing high-quality images, but a fundamental challenge remains: creating backgrounds that naturally accommodate text placement without compromising image quality. This…

Computer Vision and Pattern Recognition · Computer Science 2025-05-14 Tianyi Liang , Jiangqi Liu , Yifei Huang , Shiqi Jiang , Jianshen Shi , Changbo Wang , Chenhui Li

We present a new perspective of achieving image synthesis by viewing this task as a visual token generation problem. Different from existing paradigms that directly synthesize a full image from a single input (e.g., a latent code), the new…

Computer Vision and Pattern Recognition · Computer Science 2021-12-21 Yanhong Zeng , Huan Yang , Hongyang Chao , Jianbo Wang , Jianlong Fu

In this paper, we propose a novel controllable text-to-image generative adversarial network (ControlGAN), which can effectively synthesise high-quality images and also control parts of the image generation according to natural language…

Computer Vision and Pattern Recognition · Computer Science 2019-12-20 Bowen Li , Xiaojuan Qi , Thomas Lukasiewicz , Philip H. S. Torr

In this work, we introduce FlexGen, a flexible framework designed to generate controllable and consistent multi-view images, conditioned on a single-view image, or a text prompt, or both. FlexGen tackles the challenges of controllable…

Computer Vision and Pattern Recognition · Computer Science 2024-10-15 Xinli Xu , Wenhang Ge , Jiantao Lin , Jiawei Feng , Lie Xu , HanFeng Zhao , Shunsi Zhang , Ying-Cong Chen

While modern diffusion models excel at generating high-quality and diverse images, they still struggle with high-fidelity compositional and multimodal control, particularly when users simultaneously specify text prompts, subject references,…

Computer Vision and Pattern Recognition · Computer Science 2025-11-27 Yusuf Dalva , Guocheng Gordon Qian , Maya Goldenberg , Tsai-Shien Chen , Kfir Aberman , Sergey Tulyakov , Pinar Yanardag , Kuan-Chieh Jackson Wang

Text-to-Image (T2I) generation has long been an open problem, with compositional synthesis remaining particularly challenging. This task requires accurate rendering of complex scenes containing multiple objects that exhibit diverse…

Computer Vision and Pattern Recognition · Computer Science 2025-11-25 Shijian Wang , Runhao Fu , Siyi Zhao , Qingqin Zhan , Xingjian Wang , Jiarui Jin , Yuan Lu , Hanqian Wu , Cunjian Chen

Recently, we have seen a surge of personalization methods for text-to-image (T2I) diffusion models to learn a concept using a few images. Existing approaches, when used for face personalization, suffer to achieve convincing inversion with…

Computer Vision and Pattern Recognition · Computer Science 2024-08-12 Rishubh Parihar , Sachidanand VS , Sabariswaran Mani , Tejan Karmali , R. Venkatesh Babu

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…

Computer Vision and Pattern Recognition · Computer Science 2024-08-05 Dong Huo , Zixin Guo , Xinxin Zuo , Zhihao Shi , Juwei Lu , Peng Dai , Songcen Xu , Li Cheng , Yee-Hong Yang

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

As large language models have demonstrated impressive performance in many domains, recent works have adopted language models (LMs) as controllers of visual modules for vision-and-language tasks. While existing work focuses on equipping LMs…

Computer Vision and Pattern Recognition · Computer Science 2023-10-30 Jaemin Cho , Abhay Zala , Mohit Bansal

Text-to-video generation has trailed behind text-to-image generation in terms of quality and diversity, primarily due to the inherent complexities of spatio-temporal modeling and the limited availability of video-text datasets. Recent…

Computer Vision and Pattern Recognition · Computer Science 2024-10-04 Xiefan Guo , Jinlin Liu , Miaomiao Cui , Liefeng Bo , Di Huang

Controllable text-to-image (T2I) diffusion models generate images conditioned on both text prompts and semantic inputs of other modalities like edge maps. Nevertheless, current controllable T2I methods commonly face challenges related to…

Computer Vision and Pattern Recognition · Computer Science 2024-05-24 Xuehai He , Jian Zheng , Jacob Zhiyuan Fang , Robinson Piramuthu , Mohit Bansal , Vicente Ordonez , Gunnar A Sigurdsson , Nanyun Peng , Xin Eric Wang

Modern generative models have demonstrated the ability to solve challenging mathematical problems. In many real-world settings, however, mathematical solutions must be expressed visually through diagrams, plots, geometric constructions, and…

Computer Vision and Pattern Recognition · Computer Science 2026-04-01 Ruiyao Liu , Hui Shen , Ping Zhang , Yunta Hsieh , Yifan Zhang , Jing Xu , Sicheng Chen , Junchen Li , Jiawei Lu , Jianing Ma , Jiaqi Mo , Qi Han , Zhen Zhang , Zhongwei Wan , Jing Xiong , Xin Wang , Ziyuan Liu , Hangrui Cao , Ngai Wong

When humans read a specific text, they often visualize the corresponding images, and we hope that computers can do the same. Text-to-image synthesis (T2I), which focuses on generating high-quality images from textual descriptions, has…

Computer Vision and Pattern Recognition · Computer Science 2024-11-26 Nonghai Zhang , Hao Tang

Sketch-to-image (S2I) translation plays an important role in image synthesis and manipulation tasks, such as photo editing and colorization. Some specific S2I translation including sketch-to-photo and sketch-to-painting can be used as…

Computer Vision and Pattern Recognition · Computer Science 2020-06-19 Jialu Huang , Jing Liao , Zhifeng Tan , Sam Kwong

Generating consistent long videos is a complex challenge: while diffusion-based generative models generate visually impressive short clips, extending them to longer durations often leads to memory bottlenecks and long-term inconsistency. In…

Computer Vision and Pattern Recognition · Computer Science 2025-07-22 Wenqi Ouyang , Zeqi Xiao , Danni Yang , Yifan Zhou , Shuai Yang , Lei Yang , Jianlou Si , Xingang Pan

Despite the significant advancements in text-to-image (T2I) generative models, users often face a trial-and-error challenge in practical scenarios. This challenge arises from the complexity and uncertainty of tedious steps such as crafting…

Computer Vision and Pattern Recognition · Computer Science 2024-11-27 Chengyou Jia , Changliang Xia , Zhuohang Dang , Weijia Wu , Hangwei Qian , Minnan Luo
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