TextLap: Customizing Language Models for Text-to-Layout Planning
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912075058511872 |
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| author | Chen, Jian Zhang, Ruiyi Zhou, Yufan Healey, Jennifer Gu, Jiuxiang Xu, Zhiqiang Chen, Changyou |
| author_facet | Chen, Jian Zhang, Ruiyi Zhou, Yufan Healey, Jennifer Gu, Jiuxiang Xu, Zhiqiang Chen, Changyou |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12844 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TextLap: Customizing Language Models for Text-to-Layout Planning Chen, Jian Zhang, Ruiyi Zhou, Yufan Healey, Jennifer Gu, Jiuxiang Xu, Zhiqiang Chen, Changyou Computation and Language Machine Learning 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. |
| title | TextLap: Customizing Language Models for Text-to-Layout Planning |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2410.12844 |