Automatic generation of graphical layouts is crucial for many real-world applications, including designing posters, flyers, advertisements, and graphical user interfaces. Given the incredible ability of Large language models (LLMs) in both natural language understanding and generation, we believe that we could customize an LLM to help people create compelling graphical layouts starting with only text instructions from the user. We call our method TextLap (text-based layout planning). It uses a curated instruction-based layout planning dataset (InsLap) to customize LLMs as a graphic designer. We demonstrate the effectiveness of TextLap and show that it outperforms strong baselines, including GPT-4 based methods, for image generation and graphical design benchmarks.
@article{arxiv.2410.12844,
title = {TextLap: Customizing Language Models for Text-to-Layout Planning},
author = {Jian Chen and Ruiyi Zhang and Yufan Zhou and Jennifer Healey and Jiuxiang Gu and Zhiqiang Xu and Changyou Chen},
journal= {arXiv preprint arXiv:2410.12844},
year = {2024}
}