Constrained Graphic Layout Generation via Latent Optimization

Kotaro Kikuchi, Edgar Simo-Serra, Mayu Otani, Kota Yamaguchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)


It is common in graphic design humans visually arrange various elements according to their design intent and semantics. For example, a title text almost always appears on top of other elements in a document. In this work, we generate graphic layouts that can flexibly incorporate such design semantics, either specified implicitly or explicitly by a user. We optimize using the latent space of an off-the-shelf layout generation model, allowing our approach to be complementary to and used with existing layout generation models. Our approach builds on a generative layout model based on a Transformer architecture, and formulates the layout generation as a constrained optimization problem where design constraints are used for element alignment, overlap avoidance, or any other user-specified relationship. We show in the experiments that our approach is capable of generating realistic layouts in both constrained and unconstrained generation tasks with a single model. The code is available at

Original languageEnglish
Title of host publicationMM 2021 - Proceedings of the 29th ACM International Conference on Multimedia
PublisherAssociation for Computing Machinery, Inc
Number of pages9
ISBN (Electronic)9781450386517
Publication statusPublished - 2021 Oct 17
Event29th ACM International Conference on Multimedia, MM 2021 - Virtual, Online, China
Duration: 2021 Oct 202021 Oct 24

Publication series

NameMM 2021 - Proceedings of the 29th ACM International Conference on Multimedia


Conference29th ACM International Conference on Multimedia, MM 2021
CityVirtual, Online


  • constrained optimization
  • generative adversarial network
  • latent space exploration
  • layout generation

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Software
  • Computer Graphics and Computer-Aided Design


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