DesignTaste：将 AI 生成视觉设计与获奖作品对比的评测基准

Authors:Zhou Chen, JiaXiang Li, DanYing Pan, Ye Li, TianRui Yang, EnBao Zhang, YaNan Xiao, LiXin Sun, Anqi Liu, Dong Zhao, JianYuan Li, YuanYuan Ding, Kai Kong, Xinwen Xu, HuiTang Du, Min Zhou, Zhiqiang Li

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Abstract:Text-to-image benchmarks commonly assess text-image alignment or overall preference. Whether models can produce good designs when a brief leaves the appearance open remains less well understood. We introduce DesignTaste, a visual design benchmark that compares generated images with award-winning works. From these works and their publicly available context, we derive 403 design briefs: 100 poster tasks from 100 Beste Plakate 2025 and 303 tasks spanning 20 categories from ADC 2026. Six leading commercial text-to-image models each generate an image from the same brief. A single model judge scores each output and the corresponding award-winning work independently on five evaluation dimensions. On each dataset, only ArtArk achieves a higher mean overall score than the award-winning works. All six models score lower in Creative Originality on both datasets. These results show that producing finished, visually appealing images does not imply that a model can develop good design solutions.

Comments:	31 pages, 12 figures, including appendices. Code and data: this https URL
Subjects:	Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2611.16442 [cs.CV]
	(or arXiv:2611.16442v1 [cs.CV] for this version)
	https://doi.org/10.48550/arXiv.2611.16442 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Zhou Chen [view email]
[v1] Thu, 12 Nov 2026 07:52:27 UTC (30,443 KB)