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arXiv · 2301.02363

Text2Poster: Laying out Stylized Texts on Retrieved Images

Chuhao Jin·Hongteng Xu·Ruihua Song·Zhiwu Lu
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Abstract

Poster generation is a significant task for a wide range of applications, which is often time-consuming and requires lots of manual editing and artistic experience. In this paper, we propose a novel data-driven framework, called \textit{Text2Poster}, to automatically generate visually-effective posters from textual information. Imitating the process of manual poster editing, our framework leverages a large-scale pretrained visual-textual model to retrieve background images from given texts, lays out the texts on the images iteratively by cascaded auto-encoders, and finally, stylizes the texts by a matching-based method. We learn the modules of the framework by weakly- and self-supervised learning strategies, mitigating the demand for labeled data. Both objective and subjective experiments demonstrate that our Text2Poster outperforms state-of-the-art methods, including academic research and commercial software, on the quality of generated posters.

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Chuhao Jin, Hongteng Xu, Ruihua Song, Zhiwu Lu. 2023-01-06. Text2Poster: Laying out Stylized Texts on Retrieved Images. https://doi.org/10.1109/icassp43922.2022.9747465

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