Advertising posters, a form of information presentation, combine visual and
linguistic modalities. Creating a poster involves multiple steps and
necessitates design experience and creativity. This paper introduces
AutoPoster, a highly automatic and content-aware system for generating
advertising posters. With only product images and titles as inputs, AutoPoster
can automatically produce posters of varying sizes through four key stages:
image cleaning and retargeting, layout generation, tagline generation, and
style attribute prediction. To ensure visual harmony of posters, two
content-aware models are incorporated for layout and tagline generation.
Moreover, we propose a novel multi-task Style Attribute Predictor (SAP) to
jointly predict visual style attributes. Meanwhile, to our knowledge, we
propose the first poster generation dataset that includes visual attribute
annotations for over 76k posters. Qualitative and quantitative outcomes from
user studies and experiments substantiate the efficacy of our system and the
aesthetic superiority of the generated posters compared to other poster
generation methods.

该研究介绍了一种高度自动化和内容感知的广告海报生成系统 AutoPoster，通过图像清理和重定向、布局生成、标语生成和样式属性预测等四个关键阶段，该系统可以仅凭产品图像和标题自动生成不同尺寸的海报。通过融合两个内容感知模型用于布局和标语生成，同时提出了一种用于联合预测视觉样式属性的新型多任务样式属性预测器 (SAP)。此外，我们提出了首个包含超过 76k 海报视觉属性注释的海报生成数据集。用户研究和实验证明了我们的系统的有效性以及生成的海报在美学上相较其他生成方法的优势。