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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

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

Diffusion-based generative models have significantly advanced text-to-image generation but encounter challenges when processing lengthy and intricate text prompts describing complex scenes with multiple objects. While excelling in…

Computer Vision and Pattern Recognition · Computer Science 2024-02-27 Hanan Gani , Shariq Farooq Bhat , Muzammal Naseer , Salman Khan , Peter Wonka

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

Recent text-to-image (T2I) models have made remarkable progress in generating visually realistic and semantically coherent images. However, they still suffer from randomness and inconsistency with the given prompts, particularly when…

Computer Vision and Pattern Recognition · Computer Science 2026-03-31 Kaishen Wang , Ruibo Chen , Tong Zheng , Heng Huang

The burgeoning field of generative artificial intelligence has fundamentally reshaped our approach to content creation, with Large Vision-Language Models (LVLMs) standing at its forefront. While current LVLMs have demonstrated impressive…

Computer Vision and Pattern Recognition · Computer Science 2025-07-08 Spencer Ramsey , Jeffrey Lee , Amina Grant

State-of-the-art T2I models are capable of generating high-resolution images given textual prompts. However, they still struggle with accurately depicting compositional scenes that specify multiple objects, attributes, and spatial…

Computer Vision and Pattern Recognition · Computer Science 2025-05-19 Yixin Wan , Kai-Wei Chang

Designing realistic multi-object scenes requires not only generating images, but also planning spatial layouts that respect semantic relations and physical plausibility. On one hand, while recent advances in diffusion models have enabled…

Computer Vision and Pattern Recognition · Computer Science 2025-09-30 Zezhong Fan , Xiaohan Li , Luyi Ma , Kai Zhao , Liang Peng , Topojoy Biswas , Evren Korpeoglu , Kaushiki Nag , Kannan Achan

Image composition and generation are processes where the artists need control over various parts of the generated images. However, the current state-of-the-art generation models, like Stable Diffusion, cannot handle fine-grained part-level…

Computer Vision and Pattern Recognition · Computer Science 2025-07-01 Harsh Rangwani , Aishwarya Agarwal , Kuldeep Kulkarni , R. Venkatesh Babu , Srikrishna Karanam

Recent advancements in text-to-image models, particularly diffusion models, have shown significant promise. However, compositional text-to-image models frequently encounter difficulties in generating high-quality images that accurately…

Computer Vision and Pattern Recognition · Computer Science 2023-10-11 Song Wen , Guian Fang , Renrui Zhang , Peng Gao , Hao Dong , Dimitris Metaxas

Despite the remarkable capabilities of text-to-image (T2I) generation models, real-world applications often demand fine-grained, iterative image editing that existing methods struggle to provide. Key challenges include granular instruction…

Computer Vision and Pattern Recognition · Computer Science 2025-08-26 Zihan Liang , Jiahao Sun , Haoran Ma

In the text-to-image generation field, recent remarkable progress in Stable Diffusion makes it possible to generate rich kinds of novel photorealistic images. However, current models still face misalignment issues (e.g., problematic spatial…

Computer Vision and Pattern Recognition · Computer Science 2023-08-15 Leigang Qu , Shengqiong Wu , Hao Fei , Liqiang Nie , Tat-Seng Chua

Diffusion models have exhibited substantial success in text-to-image generation. However, they often encounter challenges when dealing with complex and dense prompts involving multiple objects, attribute binding, and long descriptions. In…

Computer Vision and Pattern Recognition · Computer Science 2024-08-28 Mushui Liu , Yuhang Ma , Yang Zhen , Jun Dan , Yunlong Yu , Zeng Zhao , Zhipeng Hu , Bai Liu , Changjie Fan

We present Comp-X, the first intelligently interactive image compression paradigm empowered by the impressive reasoning capability of large language model (LLM) agent. Notably, commonly used image codecs usually suffer from limited coding…

Computer Vision and Pattern Recognition · Computer Science 2025-08-22 Yixin Gao , Xin Li , Xiaohan Pan , Runsen Feng , Bingchen Li , Yunpeng Qi , Yiting Lu , Zhengxue Cheng , Zhibo Chen , Jörn Ostermann

Visual compliance verification is a critical yet underexplored problem in computer vision, especially in domains such as media, entertainment, and advertising where content must adhere to complex and evolving policy rules. Existing methods…

Computer Vision and Pattern Recognition · Computer Science 2026-03-23 Rahul Ghosh , Baishali Chaudhury , Hari Prasanna Das , Meghana Ashok , Ryan Razkenari , Long Chen , Sungmin Hong , Chun-Hao Liu

Despite their impressive realism, modern text-to-image models still struggle with compositionality, often failing to render accurate object counts, attributes, and spatial relations. To address this challenge, we present a training-free…

Computer Vision and Pattern Recognition · Computer Science 2026-03-30 Minsuk Ji , Sanghyeok Lee , Namhyuk Ahn

Despite recent progress in text-to-image (T2I) generation, existing models often struggle to faithfully capture user intentions from short and under-specified prompts. While prior work has attempted to enhance prompts using large language…

Computer Vision and Pattern Recognition · Computer Science 2025-05-26 Mingrui Wu , Lu Wang , Pu Zhao , Fangkai Yang , Jianjin Zhang , Jianfeng Liu , Yuefeng Zhan , Weihao Han , Hao Sun , Jiayi Ji , Xiaoshuai Sun , Qingwei Lin , Weiwei Deng , Dongmei Zhang , Feng Sun , Qi Zhang , Rongrong Ji

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-video generation models have shown significant progress in the recent years. However, they still struggle with generating complex dynamic scenes based on compositional text prompts, such as attribute binding for multiple objects,…

Computer Vision and Pattern Recognition · Computer Science 2024-12-06 Kaiyi Huang , Yukun Huang , Xuefei Ning , Zinan Lin , Yu Wang , Xihui Liu

Text-to-image (T2I) generation has made remarkable progress, yet existing systems still lack intuitive control over spatial composition, object consistency, and multi-step editing. We present $\textbf{LayerCraft}$, a modular framework that…

Machine Learning · Computer Science 2025-10-20 Yuyao Zhang , Jinghao Li , Yu-Wing Tai
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