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Related papers: ReCo: Region-Controlled Text-to-Image Generation

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Achieving precise word-level typography control within generated images remains a persistent challenge. To address it, we newly construct a word-level controlled scene text dataset and introduce the Text-Image Alignment (TIA) framework.…

Computer Vision and Pattern Recognition · Computer Science 2025-06-27 Wenda Shi , Yiren Song , Zihan Rao , Dengming Zhang , Jiaming Liu , Xingxing Zou

TIPO (Text-to-Image Prompt Optimization) introduces an efficient approach for automatic prompt refinement in text-to-image (T2I) generation. Starting from simple user prompts, TIPO leverages a lightweight pre-trained model to expand these…

Computer Vision and Pattern Recognition · Computer Science 2026-02-10 Shih-Ying Yeh , Yi Li , Sang-Hyun Park , Giyeong Oh , Xuehai Wang , Min Song , Youngjae Yu , Shang-Hong Lai

Recent video generation models have revealed the emergence of Chain-of-Frame (CoF) reasoning, enabling frame-by-frame visual inference. With this capability, video models have been successfully applied to various visual tasks (e.g., maze…

Computer Vision and Pattern Recognition · Computer Science 2026-01-16 Chengzhuo Tong , Mingkun Chang , Shenglong Zhang , Yuran Wang , Cheng Liang , Zhizheng Zhao , Ruichuan An , Bohan Zeng , Yang Shi , Yifan Dai , Ziming Zhao , Guanbin Li , Pengfei Wan , Yuanxing Zhang , Wentao Zhang

Translating information between text and image is a fundamental problem in artificial intelligence that connects natural language processing and computer vision. In the past few years, performance in image caption generation has seen…

Computer Vision and Pattern Recognition · Computer Science 2017-06-06 Hao Dong , Jingqing Zhang , Douglas McIlwraith , Yike Guo

Large-scale Text-to-Image (T2I) diffusion models demonstrate significant generation capabilities based on textual prompts. Based on the T2I diffusion models, text-guided image editing research aims to empower users to manipulate generated…

Computer Vision and Pattern Recognition · Computer Science 2024-05-03 Chuanming Tang , Kai Wang , Fei Yang , Joost van de Weijer

Text-to-Image (T2I) models have made significant advancements in recent years, but they still struggle to accurately capture intricate details specified in complex compositional prompts. While fine-tuning T2I models with reward objectives…

Computer Vision and Pattern Recognition · Computer Science 2024-11-01 Luca Eyring , Shyamgopal Karthik , Karsten Roth , Alexey Dosovitskiy , Zeynep Akata

Text-to-image (T2I) models have achieved remarkable progress, yet they continue to struggle with complex prompts that require simultaneously handling multiple objects, relations, and attributes. Existing inference-time strategies, such as…

Computer Vision and Pattern Recognition · Computer Science 2026-01-22 Shantanu Jaiswal , Mihir Prabhudesai , Nikash Bhardwaj , Zheyang Qin , Amir Zadeh , Chuan Li , Katerina Fragkiadaki , Deepak Pathak

Text-to-Image (T2I) synthesis has made significant advancements in recent years, driving applications such as generating datasets automatically. However, precise control over object localization in generated images remains a challenge.…

Computer Vision and Pattern Recognition · Computer Science 2025-11-07 Biao Liu , Yuanzhi Liang

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

Current text-to-image models struggle to provide precise camera control using natural language alone. In this work, we present a framework for precise camera control with global scene understanding in text-to-image generation by learning…

Computer Vision and Pattern Recognition · Computer Science 2026-04-23 Xinxuan Lu , Charless Fowlkes , Alexander C. Berg

Text-to-image (T2I) generation aims to synthesize images from textual prompts, which jointly specify what must be shown and imply what can be inferred, which thus correspond to two core capabilities: \textbf{\textit{composition}} and…

Computer Vision and Pattern Recognition · Computer Science 2026-03-03 Ouxiang Li , Yuan Wang , Xinting Hu , Huijuan Huang , Rui Chen , Jiarong Ou , Xin Tao , Pengfei Wan , Xiaojuan Qi , Fuli Feng

Although text-to-image (T2I) models have recently thrived as visual generative priors, their reliance on high-quality text-image pairs makes scaling up expensive. We argue that grasping the cross-modality alignment is not a necessity for a…

Computer Vision and Pattern Recognition · Computer Science 2025-03-25 Shuailei Ma , Kecheng Zheng , Ying Wei , Wei Wu , Fan Lu , Yifei Zhang , Chen-Wei Xie , Biao Gong , Jiapeng Zhu , Yujun Shen

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

Current text-to-image (T2I) generation models achieve promising results, but they fail on the scenarios where the knowledge implied in the text prompt is uncertain. For example, a T2I model released in February would struggle to generate a…

Computer Vision and Pattern Recognition · Computer Science 2025-05-22 Chuanhao Li , Jianwen Sun , Yukang Feng , Mingliang Zhai , Yifan Chang , Kaipeng Zhang

Recent text-to-image diffusion models have reached an unprecedented level in generating high-quality images. However, their exclusive reliance on textual prompts often falls short in precise control of image compositions. In this paper, we…

Computer Vision and Pattern Recognition · Computer Science 2024-03-27 Peiang Zhao , Han Li , Ruiyang Jin , S. Kevin Zhou

Current text-to-image generative models struggle to accurately represent object states (e.g., "a table without a bottle," "an empty tumbler"). In this work, we first design a fully-automatic pipeline to generate high-quality synthetic data…

Computer Vision and Pattern Recognition · Computer Science 2025-05-06 Tianle Chen , Chaitanya Chakka , Deepti Ghadiyaram

Diffusion-driven text-to-image (T2I) generation has achieved remarkable advancements in recent years. To further improve T2I models' capability in numerical and spatial reasoning, layout is employed as an intermedium to bridge large…

Computer Vision and Pattern Recognition · Computer Science 2026-03-10 Yuhao Jia , Wenhan Tan

AI-based text-to-image models do not only excel at generating realistic images, they also give designers more and more fine-grained control over the image content. Consequently, these approaches have gathered increased attention within the…

Computer Vision and Pattern Recognition · Computer Science 2025-01-30 Sebastian Hartwig , Dominik Engel , Leon Sick , Hannah Kniesel , Tristan Payer , Poonam Poonam , Michael Glöckler , Alex Bäuerle , Timo Ropinski

Over the past few years, Text-to-Image (T2I) generation approaches based on diffusion models have gained significant attention. However, vanilla diffusion models often suffer from spelling inaccuracies in the text displayed within the…

Computer Vision and Pattern Recognition · Computer Science 2024-10-30 Sanyam Lakhanpal , Shivang Chopra , Vinija Jain , Aman Chadha , Man Luo

We propose YOLO-Count, a differentiable open-vocabulary object counting model that tackles both general counting challenges and enables precise quantity control for text-to-image (T2I) generation. A core contribution is the 'cardinality'…

Computer Vision and Pattern Recognition · Computer Science 2025-08-04 Guanning Zeng , Xiang Zhang , Zirui Wang , Haiyang Xu , Zeyuan Chen , Bingnan Li , Zhuowen Tu