PRISM：从数据中学习设计知识以实现风格化设计优化

摘要

平面设计常常需要探索不同的风格方向，这对非专业人士而言十分耗时。我们致力于解决基于自然语言指令实现设计风格优化的问题。尽管视觉语言模型（VLM）在平面设计领域已取得初步成果，但其预训练的风格知识往往过于笼统，且与特定领域数据存在偏差。例如，视觉语言模型可能将极简主义与抽象设计相关联，而设计师更注重造型与色彩的选择。我们的核心思路是利用设计数据——即隐含设计师创作原则的真实设计案例集合——来学习设计知识并指导风格优化。我们提出PRISM（先验知识驱动的风格优化方法），通过三个阶段构建并应用设计知识库：（1）对高方差设计进行聚类以捕捉风格内部的多样性；（2）将每个聚类总结为可操作的设计知识；（3）在推理过程中检索相关知识以实现风格感知的优化。在Crello数据集上的实验表明，PRISM在风格对齐度上以1.49的平均排名（越接近1越好）超越基线模型。用户研究进一步验证了这些结果，显示设计师对PRISM的输出具有持续偏好。

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.

PRISM：从数据中学习设计知识以实现风格化设计优化

PRISM: Learning Design Knowledge from Data for Stylistic Design Improvement

摘要

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