Continuous and Gradual Style Changes of Graphic Designs with Generative Model

Creating a high-quality layout design from scratch is difficult for novices. Therefore, novices often consult the works of other skilled designers for ideas regarding layout designs. Researchers have previously investigated methods to support the layout design process; these works mainly focused on retrieval methods for similar layout designs, or refinement of existing layouts. To enhance user creativity in designing layouts, assistance is needed for exploring various designs. Herein, we propose a novel deep generative model that enables the generation of various layout designs and guarantees continuous and gradual changes in layouts, for effectively exploring graphic designs. Accordingly, we present an adversarial training method with dual critic networks; we trained our model by a public graphic design dataset. We developed another interaction method that allows the user to change the graphic designs between two different layout styles and categories parametrically. We demonstrated the efficacy of the proposed method in generating rich layout variations with representation of latent space by comparing the layout designs generated by our model with by an existing model.