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Text-to-Image (T2I) models have shown great performance in generating images based on textual prompts. However, these models are vulnerable to unsafe input to generate unsafe content like sexual, harassment and illegal-activity images.…

Computer Vision and Pattern Recognition · Computer Science 2024-12-13 Zongyu Wu , Hongcheng Gao , Yueze Wang , Xiang Zhang , Suhang Wang

Generative modeling is widely regarded as one of the most essential problems in today's AI community, with text-to-image generation having gained unprecedented real-world impacts. Among various approaches, diffusion models have achieved…

Computer Vision and Pattern Recognition · Computer Science 2026-01-26 Xuyang Guo , Jiayan Huo , Yingyu Liang , Zhenmei Shi , Zhao Song , Jiahao Zhang , Zhen Zhuang

Text-to-image generative models have demonstrated remarkable capabilities in generating high-quality images based on textual prompts. However, crafting prompts that accurately capture the user's creative intent remains challenging. It often…

Human-Computer Interaction · Computer Science 2023-04-20 Stephen Brade , Bryan Wang , Mauricio Sousa , Sageev Oore , Tovi Grossman

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

Text-to-image (T2I) models are increasingly used in impactful real-life applications. As such, there is a growing need to audit these models to ensure that they generate desirable, task-appropriate images. However, systematically inspecting…

Computer Vision and Pattern Recognition · Computer Science 2026-01-26 Salma Abdel Magid , Weiwei Pan , Simon Warchol , Grace Guo , Junsik Kim , Mahia Rahman , Hanspeter Pfister

Current text-to-image (T2I) benchmarks evaluate models on rigid prompts, potentially underestimating true generative capabilities due to prompt sensitivity and creating biases that favor certain models while disadvantaging others. We…

Computer Vision and Pattern Recognition · Computer Science 2025-07-08 Haosheng Gan , Berk Tinaz , Mohammad Shahab Sepehri , Zalan Fabian , Mahdi Soltanolkotabi

Evaluating the quality of synthesized images remains a significant challenge in the development of text-to-image (T2I) generation. Most existing studies in this area primarily focus on evaluating text-image alignment, image quality, and…

Computer Vision and Pattern Recognition · Computer Science 2026-04-20 Ziwei Huang , Wanggui He , Quanyu Long , Yandi Wang , Haoyuan Li , Zhelun Yu , Fangxun Shu , Long Chan , Hao Jiang , Fei Wu , Leilei Gan

We introduce ``Idea to Image,'' a system that enables multimodal iterative self-refinement with GPT-4V(ision) for automatic image design and generation. Humans can quickly identify the characteristics of different text-to-image (T2I) models…

Computer Vision and Pattern Recognition · Computer Science 2024-08-15 Zhengyuan Yang , Jianfeng Wang , Linjie Li , Kevin Lin , Chung-Ching Lin , Zicheng Liu , Lijuan Wang

Text-to-image generative models have achieved remarkable visual quality but still struggle with compositionality$-$accurately capturing object relationships, attribute bindings, and fine-grained details in prompts. A key limitation is that…

Computer Vision and Pattern Recognition · Computer Science 2025-12-11 Arman Zarei , Jiacheng Pan , Matthew Gwilliam , Soheil Feizi , Zhenheng Yang

The rapid proliferation of multimodal generative models has sparked critical discussions on their reliability, fairness and potential for misuse. While text-to-image models excel at producing high-fidelity, user-guided content, they often…

Computer Vision and Pattern Recognition · Computer Science 2025-05-27 Jordan Vice , Naveed Akhtar , Leonid Sigal , Richard Hartley , Ajmal Mian

Text-to-image generation has advanced rapidly, but existing models still struggle with faithfully composing multiple objects and preserving their attributes in complex scenes. We propose coDrawAgents, an interactive multi-agent dialogue…

Computer Vision and Pattern Recognition · Computer Science 2026-03-16 Chunhan Li , Qifeng Wu , Jia-Hui Pan , Ka-Hei Hui , Jingyu Hu , Yuming Jiang , Bin Sheng , Xihui Liu , Wenjuan Gong , Zhengzhe Liu

The rapid advancement of text-to-image (T2I) models has increased the need for reliable human preference modeling, a demand further amplified by recent progress in reinforcement learning for preference alignment. However, existing…

Computer Vision and Pattern Recognition · Computer Science 2025-10-02 Yuxiang Guo , Jiang Liu , Ze Wang , Hao Chen , Ximeng Sun , Yang Zhao , Jialian Wu , Xiaodong Yu , Zicheng Liu , Emad Barsoum

Large language models have recently enabled text-to-CAD systems that synthesize parametric CAD programs (e.g., CadQuery) from natural language prompts. In practice, however, geometric descriptions can be under-specified or internally…

Machine Learning · Computer Science 2026-02-04 Bo Yuan , Zelin Zhao , Petr Molodyk , Bin Hu , Yongxin Chen

Text-to-image (T2I) models have achieved remarkable success in generating high-fidelity images, but they often fail in handling complex spatial relationships, e.g., spatial perception, reasoning, or interaction. These critical aspects are…

Computer Vision and Pattern Recognition · Computer Science 2026-01-30 Zengbin Wang , Xuecai Hu , Yong Wang , Feng Xiong , Man Zhang , Xiangxiang Chu

Text-to-image generative models often reflect the biases of the training data, leading to unequal representations of underrepresented groups. This study investigates inclusive text-to-image generative models that generate images based on…

Computer Vision and Pattern Recognition · Computer Science 2023-09-12 Cheng Zhang , Xuanbai Chen , Siqi Chai , Chen Henry Wu , Dmitry Lagun , Thabo Beeler , Fernando De la Torre

The rapid advancement of generative AI has democratized access to powerful tools such as Text-to-Image models. However, to generate high-quality images, users must still craft detailed prompts specifying scene, style, and context-often…

Multiagent Systems · Computer Science 2025-09-25 Dawei Xiang , Wenyan Xu , Kexin Chu , Tianqi Ding , Zixu Shen , Yiming Zeng , Jianchang Su , Wei Zhang

Text-to-image (T2I) models such as Stable Diffusion have advanced rapidly and are now widely used in content creation. However, these models can be misused to generate harmful content, including nudity or violence, posing significant safety…

Cryptography and Security · Computer Science 2025-06-13 Zilong Wang , Xiang Zheng , Xiaosen Wang , Bo Wang , Xingjun Ma , Yu-Gang Jiang

The rapid growth of the text-to-image (T2I) community has fostered a thriving online ecosystem of expert models, which are variants of pretrained diffusion models specialized for diverse generative abilities. Yet, existing model merging…

Artificial Intelligence · Computer Science 2026-03-24 Zhuoling Li , Hossein Rahmani , Jiarui Zhang , Yu Xue , Majid Mirmehdi , Jason Kuen , Jiuxiang Gu , Jun Liu

Modeling human-human interactions from text remains challenging because it requires not only realistic individual dynamics but also precise, text-consistent spatiotemporal coupling between agents. Currently, progress is hindered by 1)…

Computer Vision and Pattern Recognition · Computer Science 2025-10-09 Qingxuan Wu , Zhiyang Dou , Chuan Guo , Yiming Huang , Qiao Feng , Bing Zhou , Jian Wang , Lingjie Liu

Text-to-image (T2I) diffusion models have revolutionized generative modeling by producing high-fidelity, diverse, and visually realistic images from textual prompts. Despite these advances, existing models struggle with complex prompts…

Computer Vision and Pattern Recognition · Computer Science 2024-11-27 Eric Hanchen Jiang , Yasi Zhang , Zhi Zhang , Yixin Wan , Andrew Lizarraga , Shufan Li , Ying Nian Wu
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