Composition-aware Graphic Layout GAN for Visual-textual Presentation Designs
Min Zhou, Chenchen Xu, Ye Ma, Tiezheng Ge, Yuning Jiang, Weiwei Xu

TL;DR
This paper introduces CGL-GAN, a deep generative model that creates high-quality graphic layouts for visual-textual presentations by leveraging image composition and spatial information, with a novel domain alignment module to improve training-test consistency.
Contribution
The paper proposes a composition-aware graphic layout GAN with a domain alignment module, and introduces a large-scale dataset and new metrics for evaluating graphic layout synthesis.
Findings
CGL-GAN effectively synthesizes layouts aligned with image composition.
The domain alignment module reduces training-test input discrepancies.
Proposed metrics correlate well with aesthetic quality.
Abstract
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…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Computer Graphics and Visualization Techniques · Human Motion and Animation
