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.

本文关注利用基于图像内容的条件 GAN 模型生成具有图片感知性的广告海报平面设计，在现有数据集的基础上，将无监督领域自适应技术与深度学习相结合，设计了一种新的像素级鉴别器 (PDA-GAN) 来生成具有高质量图像感知的广告海报平面设计。 实验结果显示，PDA-GAN 可以实现最先进的性能，并为时尚的广告海报生成高质量的图像感知平面设计。