PRISM：从数据中学习设计知识以提升风格化设计水平

摘要

平面設計常需探索不同的風格方向，這對非專業人士而言相當耗時。我們針對基於自然語言指令實現風格化設計提升的難題提出解決方案。儘管視覺語言模型在平面設計領域已取得初步成果，但其預訓練的風格知識往往過於泛化，與特定領域數據存在偏差。例如，視覺語言模型可能將極簡主義與抽象設計關聯，而設計師更注重造型與色彩的選擇。我們的核心思路是藉助設計數據——即隱含設計師準則的真實設計案例集合——來學習設計知識並指導風格優化。本文提出PRISM（先驗信息驅動的風格化修改）框架，通過三階段構建並應用設計知識庫：（1）對高方差設計進行聚類以捕捉風格內部的多樣性；（2）將每個聚類總結為可操作的設計知識；（3）在推理階段檢索相關知識以實現風格感知的設計優化。在Crello數據集上的實驗表明，PRISM在風格對齊任務中以1.49的平均排名（越接近1越優）超越基準方法。用戶研究進一步驗證了這些結果，顯示設計師對PRISM的輸出成果有持續性偏好。

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提升风格化设计水平

PRISM: Learning Design Knowledge from Data for Stylistic Design Improvement

摘要

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