Attribute-Conditioned Layout GAN for Automatic Graphic Design

Jianan Li*, Jimei Yang, Jianming Zhang, Chang Liu, Christina Wang, Tingfa Xu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

56 Citations (Scopus)

Abstract

Modeling layout is an important first step for graphic design. Recently, methods for generating graphic layouts have progressed, particularly with Generative Adversarial Networks (GANs). However, the problem of specifying the locations and sizes of design elements usually involves constraints with respect to element attributes, such as area, aspect ratio and reading-order. Automating attribute conditional graphic layouts remains a complex and unsolved problem. In this article, we introduce Attribute-conditioned Layout GAN to incorporate the attributes of design elements for graphic layout generation by forcing both the generator and the discriminator to meet attribute conditions. Due to the complexity of graphic designs, we further propose an element dropout method to make the discriminator look at partial lists of elements and learn their local patterns. In addition, we introduce various loss designs following different design principles for layout optimization. We demonstrate that the proposed method can synthesize graphic layouts conditioned on different element attributes. It can also adjust well-designed layouts to new sizes while retaining elements' original reading-orders. The effectiveness of our method is validated through a user study.

Original languageEnglish
Article number9106863
Pages (from-to)4039-4048
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Volume27
Issue number10
DOIs
Publication statusPublished - 1 Oct 2021

Keywords

  • Generative adversarial networks
  • attribute
  • graphic design

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