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In this paper, we present an empirical study introducing a nuanced evaluation framework for text-to-image (T2I) generative models, applied to human image synthesis. Our framework categorizes evaluations into two distinct groups: first,…

Computer Vision and Pattern Recognition · Computer Science 2024-10-29 Muxi Chen , Yi Liu , Jian Yi , Changran Xu , Qiuxia Lai , Hongliang Wang , Tsung-Yi Ho , Qiang Xu

The recent wave of large-scale text-to-image diffusion models has dramatically increased our text-based image generation abilities. These models can generate realistic images for a staggering variety of prompts and exhibit impressive…

Machine Learning · Computer Science 2023-09-14 Alexander C. Li , Mihir Prabhudesai , Shivam Duggal , Ellis Brown , Deepak Pathak

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

Recent advancements in image generation have enabled the creation of high-quality images from text conditions. However, when facing multi-modal conditions, such as text combined with reference appearances, existing methods struggle to…

Computer Vision and Pattern Recognition · Computer Science 2024-06-14 Yucheng Han , Rui Wang , Chi Zhang , Juntao Hu , Pei Cheng , Bin Fu , Hanwang Zhang

Contents generated by recent advanced Text-to-Image (T2I) diffusion models are sometimes too imaginative for existing off-the-shelf dense predictors to estimate due to the immitigable domain gap. We introduce DMP, a pipeline utilizing…

Computer Vision and Pattern Recognition · Computer Science 2024-04-04 Hsin-Ying Lee , Hung-Yu Tseng , Hsin-Ying Lee , Ming-Hsuan Yang

Text-to-Image (T2I) generation is enabling new applications that support creators, designers, and general end users of productivity software by generating illustrative content with high photorealism starting from a given descriptive text as…

Computers and Society · Computer Science 2023-04-14 Ranjita Naik , Besmira Nushi

While text-to-image (T2I) models can synthesize high-quality images, their performance degrades significantly when prompted with novel or out-of-distribution (OOD) entities due to inherent knowledge cutoffs. We introduce World-To-Image, a…

Computer Vision and Pattern Recognition · Computer Science 2025-10-07 Moo Hyun Son , Jintaek Oh , Sun Bin Mun , Jaechul Roh , Sehyun Choi

Recent advancements in controllable text-to-image (T2I) diffusion models, such as Ctrl-X and FreeControl, have demonstrated robust spatial and appearance control without requiring auxiliary module training. However, these models often…

Computer Vision and Pattern Recognition · Computer Science 2025-08-14 Jiwon Kim , Pureum Kim , SeonHwa Kim , Soobin Park , Eunju Cha , Kyong Hwan Jin

With the open-sourcing of text-to-image models (T2I) such as stable diffusion (SD) and stable diffusion XL (SD-XL), there is an influx of models fine-tuned in specific domains based on the open-source SD model, such as in anime, character…

Computer Vision and Pattern Recognition · Computer Science 2023-12-20 Ruyi Gan , Xiaojun Wu , Junyu Lu , Yuanhe Tian , Dixiang Zhang , Ziwei Wu , Renliang Sun , Chang Liu , Jiaxing Zhang , Pingjian Zhang , Yan Song

Text-guided image manipulation with diffusion models enables flexible and precise editing based on prompts, but raises ethical and copyright concerns due to potential unauthorized modifications. To address this, we propose SecureT2I, a…

Cryptography and Security · Computer Science 2025-07-08 Xiaodong Wu , Xiangman Li , Qi Li , Jianbing Ni , Rongxing Lu

The field of text-to-image (T2I) generation has garnered significant attention both within the research community and among everyday users. Despite the advancements of T2I models, a common issue encountered by users is the need for…

Computation and Language · Computer Science 2023-10-31 Wanrong Zhu , Xinyi Wang , Yujie Lu , Tsu-Jui Fu , Xin Eric Wang , Miguel Eckstein , William Yang Wang

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

Diffusion models have attracted significant attention due to the remarkable ability to create content and generate data for tasks like image classification. However, the usage of diffusion models to generate the high-quality object…

Computer Vision and Pattern Recognition · Computer Science 2024-02-20 Kai Chen , Enze Xie , Zhe Chen , Yibo Wang , Lanqing Hong , Zhenguo Li , Dit-Yan Yeung

As recent advances in large-scale Text-to-Image (T2I) diffusion models have yielded remarkable high-quality image generation, diverse downstream Image-to-Image (I2I) applications have emerged. Despite the impressive results achieved by…

Computer Vision and Pattern Recognition · Computer Science 2024-10-11 Geonung Kim , Beomsu Kim , Eunhyeok Park , Sunghyun Cho

While recent text-to-image models can generate photorealistic images from text prompts that reflect detailed instructions, they still face significant challenges in accurately rendering words in the image. In this paper, we propose to…

Computer Vision and Pattern Recognition · Computer Science 2025-05-01 Wataru Shimoda , Naoto Inoue , Daichi Haraguchi , Hayato Mitani , Seiichi Uchida , Kota Yamaguchi

The revolution of artificial intelligence content generation has been rapidly accelerated with the booming text-to-image (T2I) diffusion models. Within just two years of development, it was unprecedentedly of high-quality, diversity, and…

Artificial Intelligence · Computer Science 2023-10-16 Zeqiang Lai , Xizhou Zhu , Jifeng Dai , Yu Qiao , Wenhai Wang

Recent advancements in text-to-image generation using diffusion models have significantly improved the quality of generated images and expanded the ability to depict a wide range of objects. However, ensuring that these models adhere…

Computer Vision and Pattern Recognition · Computer Science 2024-05-20 Michail Tarasiou , Stylianos Moschoglou , Jiankang Deng , Stefanos Zafeiriou

Text-to-image (T2I) generative models, such as Stable Diffusion and DALL-E, have shown remarkable proficiency in producing high-quality, realistic, and natural images from textual descriptions. However, these models sometimes fail to…

Computer Vision and Pattern Recognition · Computer Science 2025-03-24 Arash Marioriyad , Parham Rezaei , Mahdieh Soleymani Baghshah , Mohammad Hossein Rohban

Recent advances in text-to-image (T2I) diffusion models have facilitated creative and photorealistic image synthesis. By varying the random seeds, we can generate many images for a fixed text prompt. Technically, the seed controls the…

Computer Vision and Pattern Recognition · Computer Science 2025-04-18 Katherine Xu , Lingzhi Zhang , Jianbo Shi

Recent progress in text-to-image (TTI) systems, such as StableDiffusion, Imagen, and DALL-E 2, have made it possible to create realistic images with simple text prompts. It is tempting to use these systems to eliminate the manual task of…

Computer Vision and Pattern Recognition · Computer Science 2023-11-02 David Marwood , Shumeet Baluja , Yair Alon