Research
DesignTaste: Benchmarking AI-Generator Visual Design Against Award-Wining Works
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 in
- arXiv
- 2611.16442
- Published
- 2026-11-12
- 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
Authors’ 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…
The full abstract on arXiv