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.

翻译：广告海报作为一种信息呈现形式，融合了视觉与语言模态。制作海报涉及多个步骤，需要设计经验与创造力。本文介绍AutoPoster——一种高自动化且具有内容感知能力的广告海报生成系统。仅需输入产品图像与标题，AutoPoster即可通过四个关键阶段自动生成不同尺寸的海报：图像清洗与尺寸调整、布局生成、标语生成以及风格属性预测。为确保海报视觉和谐，我们在布局与标语生成模块中融入了两种内容感知模型。此外，我们提出了一种新颖的多任务风格属性预测器（SAP），用于联合预测视觉风格属性。同时，据我们所知，本文首次构建了包含超过7.6万张海报视觉属性标注的海报生成数据集。用户研究与实验的定性与定量结果证实了本系统的有效性，且生成海报在美学上优于其他海报生成方法。