---
title: "Scan-and-Print: Patch-level Data Summarization and Augmentation for Content-aware Layout Generation in Poster Design"
authors:
  - "HsiaoYuan Hsu"
  - "Yuxin Peng"
doi: "10.24963/ijcai.2025/1121"
url: "https://ijcai.org/proceedings/2025/1121"
pdf: "https://ijcai.org/proceedings/2025/1121.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "10090-10098"
track: "AI, Arts & Creativity"
keywords:
  - "Application domains: Images, movies and visual arts"
  - "Theory and philosophy of arts and creativity in AI systems: Autonomous creative or artistic AI"
  - "Methods and resources: Machine learning, deep learning, neural models, reinforcement learning"
---

# Scan-and-Print: Patch-level Data Summarization and Augmentation for Content-aware Layout Generation in Poster Design

HsiaoYuan Hsu, Yuxin Peng

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 10090-10098. DOI: [10.24963/ijcai.2025/1121](https://doi.org/10.24963/ijcai.2025/1121)

## Abstract

In AI-empowered poster design, content-aware layout generation is crucial for the on-image arrangement of visual-textual elements, e.g., logo, text, and underlay. To perceive the background images, existing work demanded a high parameter count that far exceeds the size of available training data, which has impeded the model's real-time performance and generalization ability. To address these challenges, we proposed a patch-level data summarization and augmentation approach, vividly named Scan-and-Print. Specifically, the scan procedure selects only the patches suitable for placing element vertices to perform fine-grained perception efficiently. Then, the print procedure mixes up the patches and vertices across two image-layout pairs to synthesize over 100% new samples in each epoch while preserving their plausibility. Besides, to facilitate the vertex-level operations, a vertex-based layout representation is introduced. Extensive experimental results on widely used benchmarks demonstrated that Scan-and-Print can generate visually appealing layouts with state-of-the-art quality while dramatically reducing computational bottleneck by 95.2%. The project page is at https://thekinsley.github.io/Scan-and-Print/.