  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.
