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

Composition-aware Graphic Layout GAN for Visual-textual Presentation Designs