Dual discriminator GANs with multi-focus label matching for image-aware layout generation

Abstract

Image-aware layout generation involves arranging graphic elements, including logo, text, underlay, and embellishment, at the appropriate position on the canvas, constituting a fundamental step in poster design. This task requires considering both the relationships among elements and the interaction between elements and images. However, existing layout generation models struggle to simultaneously satisfy explicit aesthetic principles like alignment and non-overlapping, along with implicit aesthetic principles related to the harmonious composition of images and elements. To overcome these challenges, this paper designs a GAN with dual discriminators, called DD-GAN, to generate graphic layouts according to image contents. In addition, we introduce a multi-focus label matching method to provide richer supervision and optimize model training. The incorporation of multi-focus label matching not only accelerates convergence during training but also enables the model to better capture both explicit and implicit aesthetic principles in image-aware layout generation. Quantitative and qualitative evaluations consistently demonstrate that DD-GAN, coupled with multi-focus label matching, achieves state-of-the-art performance, producing high-quality image-aware graphic layouts for advertising posters.