用真实设计数据学习风格知识，让AI更懂设计师的审美偏好。
PRISM: Learning Design Knowledge from Data for Stylistic Design Improvement
- 从真实设计作品中挖掘风格知识，构建可指导改进的设计库
- 在Crello数据集上风格匹配平均排名达1.49，优于现有方法
- 适合希望提升设计一致性与专业感的非专家用户
图形设计常需探索多种风格方向，对非专业人士而言耗时费力。本文针对基于自然语言指令的风格化设计改进问题，提出PRISM（先验引导风格修改）框架。现有视觉语言模型虽在设计任务上有初步成效，但其预训练风格知识过于泛化且与特定领域数据不匹配——例如将极简主义关联为抽象图像，而设计师更关注形状与色彩选择。本研究的核心洞察是利用真实设计数据（隐含设计师原则）来学习设计知识并指导风格优化。PRISM通过三阶段实现：(1) 对高差异性设计进行聚类以捕捉风格内部多样性；(2) 将每类聚簇总结为可操作的设计知识；(3) 推理时检索相关知识，实现风格感知的改进。在Crello数据集上的实验表明，PRISM在风格对齐度上达到平均排名1.49（越接近1越好），显著优于基线。用户研究进一步验证，设计师一致更偏好PRISM生成结果。
原文摘要 · Abstract (English)
Graphic design often involves exploring different stylistic directions, which can be time-consuming for non-experts. We address this problem of stylistically improving designs based on natural language instructions. While VLMs have shown initial success in graphic design, their pretrained knowledge on styles is often too general and misaligned with specific domain data. For example, VLMs may associate minimalism with abstract designs, whereas designers emphasize shape and color choices. Our key insight is to leverage design data -- a collection of real-world designs that implicitly capture designer's principles -- to learn design knowledge and guide stylistic improvement. We propose PRISM (PRior-Informed Stylistic Modification) that constructs and applies a design knowledge base through three stages: (1) clustering high-variance designs to capture diversity within a style, (2) summarizing each cluster into actionable design knowledge, and (3) retrieving relevant knowledge during inference to enable style-aware improvement. Experiments on the Crello dataset show that PRISM achieves the highest average rank of 1.49 (closer to 1 is better) over baselines in style alignment. User studies further validate these results, showing that PRISM is consistently preferred by designers.
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