评测图像生成模型对真实物体的准确表现能力，发现主流模型仍存在细节失真问题。
KITTEN: A Knowledge-Intensive Evaluation of Image Generation on Visual Entities
- 构建KITTEN基准，聚焦真实世界实体的视觉生成质量
- 先进模型在地标、动物等实体上仍难还原准确细节
- 检索增强模型依赖参考图，难生成创意新构型
文本到图像生成技术虽显著提升图像质量，但现有评估多关注美学或与文本提示的匹配度，难以判断模型是否能准确呈现多样化真实视觉实体。为此，我们提出KITTEN——一个针对真实世界实体的视觉生成知识密集型评测基准。通过该基准，我们系统评估了最新的文本到图像模型及检索增强模型在生成地标、动物等实体方面的能力。结合精心设计的人工评价、自动指标和多模态大模型（MLLM）评估，结果表明：即使最先进的文本到图像模型，在实体视觉细节上也存在明显偏差。检索增强模型虽通过引入参考图像提升了实体保真度，但过度依赖参考图，在创造性文本提示下难以生成新颖的实体组合。
原文摘要 · Abstract (English)
Recent advances in text-to-image generation have improved the quality of synthesized images, but evaluations mainly focus on aesthetics or alignment with text prompts. Thus, it remains unclear whether these models can accurately represent a wide variety of realistic visual entities. To bridge this gap, we propose KITTEN, a benchmark for Knowledge-InTensive image generaTion on real-world ENtities. Using KITTEN, we conduct a systematic study of the latest text-to-image models and retrieval-augmented models, focusing on their ability to generate real-world visual entities, such as landmarks and animals. Analysis using carefully designed human evaluations, automatic metrics, and MLLM evaluations show that even advanced text-to-image models fail to generate accurate visual details of entities. While retrieval-augmented models improve entity fidelity by incorporating reference images, they tend to over-rely on them and struggle to create novel configurations of the entity in creative text prompts.
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