mlsanityData to 7 Oct 2026

From Fragment to One Piece: A Survey on AI-Driven Graphic Design

arXiv 2503.18641 ↗First posted 24 Mar 2025cs.AIcited by 2

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Abstract (arXiv)

This survey provides a comprehensive overview of the advancements in Artificial Intelligence in Graphic Design (AIGD), focusing on integrating AI techniques to support design interpretation and enhance the creative process. We categorize the field into two primary directions: perception tasks, which involve understanding and analyzing design elements, and generation tasks, which focus on creating new design elements and layouts. The survey covers various subtasks, including visual element perception and generation, aesthetic and semantic understanding, layout analysis, and generation. We highlight the role of large language models and multimodal approaches in bridging the gap between localized visual features and global design intent. Despite significant progress, challenges remain to understanding human intent, ensuring interpretability, and maintaining control over multilayered compositions. This survey serves as a guide for researchers, providing information on the current state of AIGD and potential future directionsfootnotehttps://github.com/zhangtianer521/excellentIntelligentgraphicdesign.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 126 of its 240 references that are in the map. First posted on arXiv 24 Mar 2025.

Builds on

126 of 240 references.

References · 126

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38 more
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50 most cited of 126 All 126 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025WordCon: Word-level Typography Control in Scene Text Renderingcited by 16
  2. 2026TASTE: A Designer-Annotated Multi-Dimensional Preference Dataset for AI-Generated Graphic Designcited by 0