  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.
