  In this paper, we study the graphic layout generation problem of producing
high-quality visual-textual presentation designs for given images. We note that
image compositions, which contain not only global semantics but also spatial
information, would largely affect layout results. Hence, we propose a deep
generative model, dubbed as composition-aware graphic layout GAN (CGL-GAN), to
synthesize layouts based on the global and spatial visual contents of input
images. To obtain training images from images that already contain manually
designed graphic layout data, previous work suggests masking design elements
(e.g., texts and embellishments) as model inputs, which inevitably leaves hint
of the ground truth. We study the misalignment between the training inputs
(with hint masks) and test inputs (without masks), and design a novel domain
alignment module (DAM) to narrow this gap. For training, we built a large-scale
layout dataset which consists of 60,548 advertising posters with annotated
layout information. To evaluate the generated layouts, we propose three novel
metrics according to aesthetic intuitions. Through both quantitative and
qualitative evaluations, we demonstrate that the proposed model can synthesize
high-quality graphic layouts according to image compositions.
