VG-GAN: Conditional GAN Framework for Graphical Design Generation.

This paper introduces VG-GAN, a novel conditional GAN for graphical design generation tasks with applications in background design, layout and scene generation. VG-GAN utilizes vector-based methods to achieve scale invariance of the generated layouts. Concretely, the GAN model outputs only relevant vector layout information, and the final layout image is rendered in a post-processing step to allow layouts to be scaled arbitrarily. In contrast to existing vector-based generation models that require a choice of initial class relationships, VG-GAN proposes a selection module to automatically learn the class relationships in target applications, presenting a novel application of vector-based generation not addressed by existing literature. VG-GAN is applied on three generation tasks given optional design conditions, namely, banner background generation, document layout generation and cli-part scene generation. The results demonstrate the model’s effectiveness in learning and generating conditional graphical design.