PosterIQ: 一份面向海报理解和生成的设计视角基准
PosterIQ: A Design Perspective Benchmark for Poster Understanding and Generation
AI总结：
PosterIQ 是一个设计驱动的海报理解与生成基准，包含7765个图像注释实例和822个生成提示，评估了最先进的MLLMs和扩散生成器，揭示了视觉层次、字体语义等领域的差距。
摘要：
我们提出了PosterIQ，一个设计驱动的海报理解和生成基准，标注了构成结构、字体层级和语义意图。它包含7765个图像-注释实例和822个生成提示，涵盖真实、专业和合成案例。为弥合视觉设计认知与生成模型之间的差距，我们定义了布局解析、文本-图像对应、字体/可读性、字体感知、设计质量评估以及可控的、以构图为主的生成任务。我们评估了最先进的MLLMs和扩散基生成器，发现视觉层次、字体语义、显著性控制和意图传达存在持续差距；商业模型在高层推理上领先，但作为不敏感的自动评分者，而生成器在文本生成上表现良好，但在构图感知合成上挣扎。广泛分析显示，PosterIQ既是定量基准，也是设计推理的诊断工具，提供可重复的、任务特定的度量标准。我们旨在催化模型的创造力，并将以人类为中心的设计原则整合到生成视觉-语言系统中。
英文摘要：
We present PosterIQ, a design-driven benchmark for poster understanding and generation, annotated across composition structure, typographic hierarchy, and semantic intent. It includes 7,765 image-annotation instances and 822 generation prompts spanning real, professional, and synthetic cases. To bridge visual design cognition and generative modeling, we define tasks for layout parsing, text-image correspondence, typography/readability and font perception, design quality assessment, and controllable, composition-aware generation with metaphor. We evaluate state-of-the-art MLLMs and diffusion-based generators, finding persistent gaps in visual hierarchy, typographic semantics, saliency control, and intention communication; commercial models lead on high-level reasoning but act as insensitive automatic raters, while generators render text well yet struggle with composition-aware synthesis. Extensive analyses show PosterIQ is both a quantitative benchmark and a diagnostic tool for design reasoning, offering reproducible, task-specific metrics. We aim to catalyze models' creativity and integrate human-centred design principles into generative vision-language systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
