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Creative image generation has emerged as a compelling area of research, driven by the need to produce novel and high-quality images that expand the boundaries of imagination. In this work, we propose a novel framework for creative…

Computer Vision and Pattern Recognition · Computer Science 2026-02-04 Kunpeng Song , Ahmed Elgammal

Text-to-image generation models suffer from alignment problems, where generated images fail to accurately capture the objects and relations in the text prompt. Prior work has focused on improving alignment by refining the diffusion process,…

Computation and Language · Computer Science 2026-04-20 Guy Kaplan , Michael Toker , Yuval Reif , Yonatan Belinkov , Roy Schwartz

While generative models produce high-quality images of concepts learned from a large-scale database, a user often wishes to synthesize instantiations of their own concepts (for example, their family, pets, or items). Can we teach a model to…

Computer Vision and Pattern Recognition · Computer Science 2023-06-21 Nupur Kumari , Bingliang Zhang , Richard Zhang , Eli Shechtman , Jun-Yan Zhu

Visual metaphors are powerful rhetorical devices used to persuade or communicate creative ideas through images. Similar to linguistic metaphors, they convey meaning implicitly through symbolism and juxtaposition of the symbols. We propose a…

Computation and Language · Computer Science 2023-07-17 Tuhin Chakrabarty , Arkadiy Saakyan , Olivia Winn , Artemis Panagopoulou , Yue Yang , Marianna Apidianaki , Smaranda Muresan

Text-to-image diffusion models have achieved remarkable progress in generating diverse and realistic images from textual descriptions. However, they still struggle with personalization, which requires adapting a pretrained model to depict…

Computer Vision and Pattern Recognition · Computer Science 2025-12-01 Seoyun Yang , Gihoon Kim , Taesup Kim

In text-to-image generation, producing a series of consistent contents that preserve the same identity is highly valuable for real-world applications. Although a few works have explored training-free methods to enhance the consistency of…

Computer Vision and Pattern Recognition · Computer Science 2025-07-16 Mengyu Wang , Henghui Ding , Jianing Peng , Yao Zhao , Yunpeng Chen , Yunchao Wei

Although recent advancements in diffusion models have significantly enriched the quality of generated images, challenges remain in synthesizing pixel-based human-drawn sketches, a representative example of abstract expression. To combat…

Computer Vision and Pattern Recognition · Computer Science 2026-04-15 Jiho Park , Sieun Choi , Jaeyoon Seo , Jihie Kim

For recent diffusion-based generative models, maintaining consistent content across a series of generated images, especially those containing subjects and complex details, presents a significant challenge. In this paper, we propose a new…

Computer Vision and Pattern Recognition · Computer Science 2024-05-03 Yupeng Zhou , Daquan Zhou , Ming-Ming Cheng , Jiashi Feng , Qibin Hou

Diffusion models have exhibit exceptional performance in text-to-image generation and editing. However, existing methods often face challenges when handling complex text prompts that involve multiple objects with multiple attributes and…

Computer Vision and Pattern Recognition · Computer Science 2024-06-05 Ling Yang , Zhaochen Yu , Chenlin Meng , Minkai Xu , Stefano Ermon , Bin Cui

Text-to-image models have shown remarkable progress in generating high-quality images from user-provided prompts. Despite this, the quality of these images varies due to the models' sensitivity to human language nuances. With advancements…

Artificial Intelligence · Computer Science 2024-06-14 Xinrui Yang , Zhuohan Wang , Anthony Hu

Recent visual generative models enable story generation with consistent characters from text, but human-centric story generation faces additional challenges, such as maintaining detailed and diverse human face consistency and coordinating…

Computer Vision and Pattern Recognition · Computer Science 2025-12-30 Donghao Zhou , Jingyu Lin , Guibao Shen , Quande Liu , Jialin Gao , Lihao Liu , Lan Du , Cunjian Chen , Chi-Wing Fu , Xiaowei Hu , Pheng-Ann Heng

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

Multistep instructions, such as recipes and how-to guides, greatly benefit from visual aids, such as a series of images that accompany the instruction steps. While Large Language Models (LLMs) have become adept at generating coherent…

Computer Vision and Pattern Recognition · Computer Science 2024-05-17 João Bordalo , Vasco Ramos , Rodrigo Valério , Diogo Glória-Silva , Yonatan Bitton , Michal Yarom , Idan Szpektor , Joao Magalhaes

In spite of recent advancements in text-to-image generation, limitations persist in handling complex and imaginative prompts due to the restricted diversity and complexity of training data. This work explores how diffusion models can…

Computer Vision and Pattern Recognition · Computer Science 2024-07-18 Yi Yao , Chan-Feng Hsu , Jhe-Hao Lin , Hongxia Xie , Terence Lin , Yi-Ning Huang , Hong-Han Shuai , Wen-Huang Cheng

Text-to-image generative models have garnered immense attention for their ability to produce high-fidelity images from text prompts. Among these, Stable Diffusion distinguishes itself as a leading open-source model in this fast-growing…

Computer Vision and Pattern Recognition · Computer Science 2024-03-12 Shih-Ying Yeh , Yu-Guan Hsieh , Zhidong Gao , Bernard B W Yang , Giyeong Oh , Yanmin Gong

Text-to-image generation models~(e.g., Stable Diffusion) have achieved significant advancements, enabling the creation of high-quality and realistic images based on textual descriptions. Prompt inversion, the task of identifying the textual…

Computer Vision and Pattern Recognition · Computer Science 2026-03-06 Mingzhe Li , Kejing Xia , Gehao Zhang , Zhenting Wang , Guanhong Tao , Siqi Pan , Juan Zhai , Shiqing Ma

The text-driven image and video diffusion models have achieved unprecedented success in generating realistic and diverse content. Recently, the editing and variation of existing images and videos in diffusion-based generative models have…

Computer Vision and Pattern Recognition · Computer Science 2024-02-20 Yuyang Zhao , Enze Xie , Lanqing Hong , Zhenguo Li , Gim Hee Lee

Diffusion-based methods have achieved remarkable achievements in 2D image or 3D object generation, however, the generation of 3D scenes and even $360^{\circ}$ images remains constrained, due to the limited number of scene datasets, the…

Computer Vision and Pattern Recognition · Computer Science 2024-11-01 Weicai Ye , Chenhao Ji , Zheng Chen , Junyao Gao , Xiaoshui Huang , Song-Hai Zhang , Wanli Ouyang , Tong He , Cairong Zhao , Guofeng Zhang

The audiovisual industry is undergoing a profound transformation as it is integrating AI developments not only to automate routine tasks but also to inspire new forms of art. This paper addresses the problem of producing a virtually…

Computer Vision and Pattern Recognition · Computer Science 2026-01-26 Ruben Pascual , Mikel Sesma-Sara , Aranzazu Jurio , Daniel Paternain , Mikel Galar

Automatic font generation is an imitation task, which aims to create a font library that mimics the style of reference images while preserving the content from source images. Although existing font generation methods have achieved…

Computer Vision and Pattern Recognition · Computer Science 2023-12-20 Zhenhua Yang , Dezhi Peng , Yuxin Kong , Yuyi Zhang , Cong Yao , Lianwen Jin
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