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Understanding the physical world is a fundamental challenge in embodied AI, critical for enabling agents to perform complex tasks and operate safely in real-world environments. While Vision-Language Models (VLMs) have shown great promise in…

Computer Vision and Pattern Recognition · Computer Science 2025-01-30 Wei Chow , Jiageng Mao , Boyi Li , Daniel Seita , Vitor Guizilini , Yue Wang

With advances in the quality of text-to-image (T2I) models has come interest in benchmarking their prompt faithfulness -- the semantic coherence of generated images to the prompts they were conditioned on. A variety of T2I faithfulness…

Computer Vision and Pattern Recognition · Computer Science 2024-11-01 Michael Saxon , Fatima Jahara , Mahsa Khoshnoodi , Yujie Lu , Aditya Sharma , William Yang Wang

Recent text-to-image (T2I) models have demonstrated impressive capabilities in photorealistic synthesis and instruction following. However, their reliability in knowledge-intensive settings remains largely unexplored. Unlike natural image…

Computer Vision and Pattern Recognition · Computer Science 2026-04-27 Ran Zhao , Sheng Jin , Size Wu , Kang Liao , Zerui Gong , Zujin Guo , Yang Xiao , Wei Li

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

Recent advances in video generation models demonstrate their potential as world simulators, but they often struggle with videos deviating from physical laws, a key concern overlooked by most text-to-video benchmarks. We introduce a…

Computer Vision and Pattern Recognition · Computer Science 2025-03-06 Yongfan Chen , Xiuwen Zhu , Tianyu Li

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

Significant progress has been achieved in subject-driven text-to-image (T2I) generation, which aims to synthesize new images depicting target subjects according to user instructions. However, evaluating these models remains a significant…

Computer Vision and Pattern Recognition · Computer Science 2026-04-21 Zhenyu Hu , Qing Wang , Te Cao , Luo Liao , Longfei Lu , Liqun Liu , Shuang Li , Hang Chen , Mengge Xue , Yuan Chen , Chao Deng , Peng Shu , Huan Yu , Jie Jiang

Current image generation models produce visually compelling but scientifically implausible images, exposing a fundamental gap between visual fidelity and physical realism. In this work, we introduce ScienceT2I, an expert-annotated dataset…

Computer Vision and Pattern Recognition · Computer Science 2026-04-02 Jialuo Li , Wenhao Chai , Xingyu Fu , Haiyang Xu , Saining Xie

Text-to-Image (T2I) models have transformed visual content creation, producing highly realistic images from natural language prompts. However, concerns persist around their potential to replicate and magnify existing societal biases. To…

Computer Vision and Pattern Recognition · Computer Science 2025-06-18 Sedat Porikli , Vedat Porikli

Recent multimodal image generators such as GPT-4o, Gemini 2.0 Flash, and Gemini 2.5 Pro excel at following complex instructions, editing images and maintaining concept consistency. However, they are still evaluated by disjoint toolkits:…

Computer Vision and Pattern Recognition · Computer Science 2025-05-29 Hang Hua , Ziyun Zeng , Yizhi Song , Yunlong Tang , Liu He , Daniel Aliaga , Wei Xiong , Jiebo Luo

Impressive advances in text-to-image (T2I) generative models have yielded a plethora of high performing models which are able to generate aesthetically appealing, photorealistic images. Despite the progress, these models still struggle to…

Computer Vision and Pattern Recognition · Computer Science 2024-03-27 Oscar Mañas , Pietro Astolfi , Melissa Hall , Candace Ross , Jack Urbanek , Adina Williams , Aishwarya Agrawal , Adriana Romero-Soriano , Michal Drozdzal

Although recent text-to-image generative models have achieved impressive performance, they still often struggle with capturing the compositional complexities of prompts including attribute binding, and spatial relationships between…

Computer Vision and Pattern Recognition · Computer Science 2025-03-17 Seyed Mohammad Hadi Hosseini , Amir Mohammad Izadi , Ali Abdollahi , Armin Saghafian , Mahdieh Soleymani Baghshah

While recent text-to-image (T2I) models show impressive capabilities in synthesizing images from brief descriptions, their performance significantly degrades when confronted with long, detail-intensive prompts required in professional…

Computer Vision and Pattern Recognition · Computer Science 2025-10-14 Qirui Jiao , Daoyuan Chen , Yilun Huang , Xika Lin , Ying Shen , Yaliang Li

Text-to-Image (T2I) models are capable of generating high-quality artistic creations and visual content. However, existing research and evaluation standards predominantly focus on image realism and shallow text-image alignment, lacking a…

Computer Vision and Pattern Recognition · Computer Science 2025-11-20 Yuwei Niu , Munan Ning , Mengren Zheng , Weiyang Jin , Bin Lin , Peng Jin , Jiaqi Liao , Chaoran Feng , Kunpeng Ning , Bin Zhu , Li Yuan

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

Generative AI models, particularly Text-to-Video (T2V) systems, offer a promising avenue for transforming science education by automating the creation of engaging and intuitive visual explanations. In this work, we take a first step toward…

Computer Vision and Pattern Recognition · Computer Science 2026-01-06 Megha Mariam K. M , Aditya Arun , Zakaria Laskar , C. V. Jawahar

Recent advances in text-to-image (T2I) generation have achieved impressive results, yet existing models often struggle with simple or underspecified prompts, leading to suboptimal image-text alignment, aesthetics, and quality. We propose a…

Computation and Language · Computer Science 2025-10-16 Ruibo Chen , Jiacheng Pan , Heng Huang , Zhenheng Yang

Infographics are composite visual artifacts that combine data visualizations with textual and illustrative elements to communicate information. While recent text-to-image (T2I) models can generate aesthetically appealing images, their…

Recent years have seen impressive advances in text-to-image generation, with image generative or unified models producing high-quality images from text. Yet these models still struggle with fine-grained color controllability, often failing…

Computer Vision and Pattern Recognition · Computer Science 2025-10-24 Muhammad Atif Butt , Alexandra Gomez-Villa , Tao Wu , Javier Vazquez-Corral , Joost Van De Weijer , Kai Wang

Despite remarkable progress in Text-to-Image models, many real-world applications require generating coherent image sets with diverse consistency requirements. Existing consistent methods often focus on a specific domain with specific…

Computer Vision and Pattern Recognition · Computer Science 2025-09-26 Chengyou Jia , Xin Shen , Zhuohang Dang , Zhuohang Dang , Changliang Xia , Weijia Wu , Xinyu Zhang , Hangwei Qian , Ivor W. Tsang , Minnan Luo