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。海报创作涉及多个步骤，需要设计经验和创造力。本文介绍了AutoPoster，一种高度自动化且内容感知的广告海报生成系统。仅以产品图像和标题为输入，AutoPoster即可通过四个关键阶段自动生成不同尺寸的海报：图像清洗与重定向、布局生成、标语生成和风格属性预测。为确保海报的视觉和谐，我们集成了两种内容感知模型用于布局和标语生成。此外，我们提出了一种新颖的多任务风格属性预测器（SAP），以联合预测视觉风格属性。同时，据我们所知，我们首次提出了包含超过7.6万张海报视觉属性标注的海报生成数据集。用户研究和实验的定性与定量结果证实了我们系统的有效性，以及生成海报相比其他海报生成方法在美学上的优越性。