arXiv:2611.16442 · v1

DesignTaste: Benchmarking AI-Generator Visual Design Against Award-Wining Works

首次提交 2026-11-12 15:52

版本更新 2026-11-12 15:52

雷达观测 2026-11-13 14:15

摘要

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.

作者备注：31 pages, 12 figures, including appendices. Code and data: https://github.com/ArtArk-AI/DesignTaste