AutoPoster: A Highly Automatic and Content-aware Design System for Advertising Poster Generation
This addresses the need for automated poster design for marketers and designers, though it is incremental as it builds on existing multimodal generation techniques.
The paper tackles the problem of automatically generating advertising posters from product images and titles, introducing AutoPoster, a system that produces posters with aesthetic superiority over other methods, as validated by user studies and experiments.
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.