TY - GEN
T1 - TextLap
T2 - 2024 Findings of the Association for Computational Linguistics, EMNLP 2024
AU - Chen, Jian
AU - Zhang, Ruiyi
AU - Zhou, Yufan
AU - Healey, Jennifer
AU - Gu, Jiuxiang
AU - Xu, Zhiqiang
AU - Chen, Changyou
N1 - Publisher Copyright:
© 2024 Association for Computational Linguistics.
PY - 2024
Y1 - 2024
N2 - 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) 1. 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.
AB - 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) 1. 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.
UR - https://www.scopus.com/pages/publications/85217621354
U2 - 10.18653/v1/2024.findings-emnlp.833
DO - 10.18653/v1/2024.findings-emnlp.833
M3 - Conference contribution
AN - SCOPUS:85217621354
T3 - EMNLP 2024 - 2024 Conference on Empirical Methods in Natural Language Processing, Findings of EMNLP 2024
SP - 14275
EP - 14289
BT - EMNLP 2024 - 2024 Conference on Empirical Methods in Natural Language Processing, Findings of EMNLP 2024
A2 - Al-Onaizan, Yaser
A2 - Bansal, Mohit
A2 - Chen, Yun-Nung
PB - Association for Computational Linguistics (ACL)
Y2 - 12 November 2024 through 16 November 2024
ER -