An Intelligent Generative Model for Layout Design of Packaging Graphic based on Bidirectional Transformer and GAN

Layout design is an important step in packaging graphic design, and high-quality layout design is an important attribute to attract consumers’ attention and subsequent purchase. In order to solve the problems of time-consuming, difficult communication and strong dependency faced by manually performing package graphic design, we propose a template-free package layout generation method to achieve intelligent layout design. This method uses generative adversarial network (GAN) as a framework, and the generator and discriminator are composed of two improved transformer structures, which combines the advantages of two mainstream generative models, taking into account the rich layout variations in packaging design for generating a robust layout. We also constructed a packaging dataset PackageLayout to verify the superiority of the proposed method, which contains 2020 packaging planar images and annotation information for three categories. After ablation experiments on the homemade packaging dataset and comparison experiments with current state-of-the-art methods (CGLGAN, DSGAN), we validated the effectiveness and stability of the model. The layouts generated by our model are visually similar to the real design layouts and outperform previous models in terms of evaluation metrics. Finally, we also constructed real designs based on the predicted layouts to better understand the visual quality, which contributes to the advancement of the application of intelligent layout design models in packaging graphic design.