Unsupervised Domain Adaption with Pixel-level Discriminator for Image-aware Layout Generation
This work improves layout generation for graphic design and advertising posters, but it is incremental as it builds on existing GAN and domain adaptation techniques.
The paper tackled the problem of generating advertising poster layouts conditioned on product images by addressing the domain gap between inpainted posters and clean images, achieving state-of-the-art performance with high-quality results.
Layout is essential for graphic design and poster generation. Recently, applying deep learning models to generate layouts has attracted increasing attention. This paper focuses on using the GAN-based model conditioned on image contents to generate advertising poster graphic layouts, which requires an advertising poster layout dataset with paired product images and graphic layouts. However, the paired images and layouts in the existing dataset are collected by inpainting and annotating posters, respectively. There exists a domain gap between inpainted posters (source domain data) and clean product images (target domain data). Therefore, this paper combines unsupervised domain adaption techniques to design a GAN with a novel pixel-level discriminator (PD), called PDA-GAN, to generate graphic layouts according to image contents. The PD is connected to the shallow level feature map and computes the GAN loss for each input-image pixel. Both quantitative and qualitative evaluations demonstrate that PDA-GAN can achieve state-of-the-art performances and generate high-quality image-aware graphic layouts for advertising posters.