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 comprehensive assessment of complex semantic understanding and world knowledge integration in text-to-image generation. To address this challenge, we propose \textbf{WISE}, the first benchmark specifically designed for \textbf{W}orld Knowledge-\textbf{I}nformed \textbf{S}emantic \textbf{E}valuation. WISE moves beyond simple word-pixel mapping by challenging models with 1000 meticulously crafted prompts across 25 subdomains in cultural common sense, spatio-temporal reasoning, and natural science. To overcome the limitations of traditional CLIP metric, we introduce \textbf{WiScore}, a novel quantitative metric for assessing knowledge-image alignment. Through comprehensive testing of 20 models (10 dedicated T2I models and 10 unified multimodal models) using 1,000 structured prompts spanning 25 subdomains, our findings reveal significant limitations in their ability to effectively integrate and apply world knowledge during image generation, highlighting critical pathways for enhancing knowledge incorporation and application in next-generation T2I models. Code and data are available at \href{https://github.com/PKU-YuanGroup/WISE}{PKU-YuanGroup/WISE}.

翻译：文本到图像（T2I）模型能够生成高质量的艺术创作和视觉内容。然而，现有研究和评估标准主要侧重于图像真实性和浅层图文对齐，缺乏对文本到图像生成过程中复杂语义理解与世界知识整合能力的全面评估。为解决这一挑战，我们提出**WISE**，这是首个专门用于**世界知识驱动语义评估**的基准测试。WISE超越简单的词-像素映射，通过精心设计的1000个提示词，挑战模型在文化常识、时空推理和自然科学等25个子领域中的表现。为克服传统CLIP度量的局限性，我们引入**WiScore**——一种用于评估知识-图像对齐的新型量化指标。通过对20个模型（包括10个专用T2I模型和10个统一多模态模型）在跨越25个子领域的1000个结构化提示词上进行全面测试，我们的研究揭示了它们在图像生成过程中有效整合与应用世界知识方面的显著局限性，从而凸显了增强下一代T2I模型知识融入与应用能力的关键路径。代码与数据可在\href{https://github.com/PKU-YuanGroup/WISE}{PKU-YuanGroup/WISE}获取。