研究人员发布了首个视觉隐喻生成基准，测试了11个模型，发现即使是最好的模型在隐喻表达方面也有明显不足。
研究团队推出VMetaphor-Bench，这是首个评估文本到图像模型视觉隐喻生成的基准，包含1500个真实创意图像隐喻，分为3个级别和10个类别。研究采用混合评估框架，通过MLLM-as-judge范式，包含9594道多选题和3个感知维度的评分。对11个代表性T2I模型的评估显示，即使是专有模型在组合结构和跨域映射方面也存在困难。
Blending Concepts: Benchmarking Visual Metaphor Generation in Text-to-Image Models
Text-to-image (T2I) models have achieved remarkable success at faithfully rendering specified objects and attributes, yet their ability to produce visual metaphors, images that convey abstract ideas by combining elements from two distinct domains, remains largely unexamined. To bridge this gap, we introduce VMetaphor-Bench, the first benchmark for evaluating visual metaphor generation in T2I models. It comprises 1,500 visual metaphors curated from real-world creative imagery, organized into three levels and ten categories, with each sample paired with two prompts of differing specificity. For evaluation, we develop a hybrid framework within an MLLM-as-judge paradigm, combining a multiple-choice question (MCQ) based protocol of 9,594 questions across four levels of metaphorical fidelity with a dimension-based scoring protocol along three perceptual dimensions. Extensive evaluation of 11 representative T2I models reveals that even the strongest proprietary models struggle with compositional structuring and cross-domain mapping, key aspects of metaphorical expression, highlighting visual metaphor generation as an important frontier for future T2I research.