发表机构
Westlake University; Ant Group; Zhejiang University; Shanghai Innovation Institute; HKUST; CASIA; Wechat AI; MBZUAI; CityU; Peking University（西湖大学; 蚂蚁集团; 浙江大学; 上海创新研究院; 香港科技大学; 中国科学院自动化研究所; 微信人工智能; 穆罕默德·本·扎耶德人工智能大学; 香港城市大学; 北京大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
UltraText Bench是一个双语基准，用于评估图像生成中密集视觉文本的渲染，通过432个提示词和Q-Judger模型评估文本保真度、清晰度、空间和场景质量，发现不同模型在不同维度上的性能差异。
AI 中文摘要
密集视觉文本要求图像生成器在多个区域中再现长字符串，并保证正确的位置和可读性。随着短字符串渲染的改进，评估必须测试在更具挑战性的场景中的持续性能。我们引入了UltraText Bench，这是一个用于提示词驱动的密集视觉文本生成的双语基准。它包含432个提示词，涵盖24个真实世界场景类别和三个难度级别，中英文各占一半。每个经过人工审核的提示词提供四到十二个文本区域的精确字符串，并配有关于其内容、位置和视觉属性的结构化参考。我们使用Q-Judger视觉语言模型根据完整参考评估每张图像，报告文本保真度、文本清晰度、空间质量和场景质量。在24种模型配置中，这些维度揭示了不同的优势：Z-Image-Turbo在报告设置下比Z-Image-Base获得3.81清晰度点，同时损失14.76保真度点。性能也随工作量变化；Qwen-Image-2512的英文综合得分从L1的86.50降至L3的42.86。十名参与者参与了自动评分的人工评估。代码库：此https URL。
英文摘要
Dense visual text requires image generators to reproduce long strings across multiple regions with correct placement and legibility. As short-string rendering improves, evaluation must test sustained performance across more demanding scenes. We introduce UltraText Bench, a bilingual benchmark for prompt-only generation of dense visual text. It contains 432 prompts spanning 24 real-world scene categories and three difficulty levels, split equally between English and Chinese. Each human-reviewed prompt supplies exact strings for four to twelve text regions, paired with structured references for their content, placement, and visual attributes. We use the Q-Judger vision-language model to assess each image against the complete reference, reporting text fidelity, text clarity, spatial quality, and scene quality. Across 24 model configurations, these dimensions reveal different strengths: Z-Image-Turbo gains 3.81 clarity points over Z-Image-Base while losing 14.76 fidelity points under the reported settings. Performance also varies with workload; Qwen-Image-2512's English composite falls from 86.50 at L1 to 42.86 at L3. Ten participants took part in human evaluation of the automatic scores. Repository: https://github.com/LINs-lab/UltraText_Bench.
