LayoutEnhancer: Generating Good Indoor Layouts from Imperfect Data

Kurt Leimer, Paul Guerrero, Tomer Weiss, Przemyslaw Musialski

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

2 Scopus citations

Abstract

We address the problem of indoor layout synthesis, which is a topic of continuing research interest in computer graphics. The newest works made significant progress using data-driven generative methods; however, these approaches rely on suitable datasets. In practice, desirable layout properties may not exist in a dataset, for instance, specific expert knowledge can be missing in the data. We propose a method that combines expert knowledge, for example, knowledge about ergonomics, with a data-driven generator based on the popular Transformer architecture. The knowledge is given as differentiable scalar functions, which can be used both as weights or as additional terms in the loss function. Using this knowledge, the synthesized layouts can be biased to exhibit desirable properties, even if these properties are not present in the dataset. Our approach can also alleviate problems of lack of data and imperfections in the data. Our work aims to improve generative machine learning for modeling and provide novel tools for designers and amateurs for the problem of interior layout creation.

Original languageEnglish (US)
Title of host publicationProceedings - SIGGRAPH Asia 2022 Conference Papers
EditorsStephen N. Spencer
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450394703
DOIs
StatePublished - Nov 29 2022
EventSIGGRAPH Asia 2022 - Computer Graphics and Interactive Techniques Conference - Asia, SA 2022 - Daegu, Korea, Republic of
Duration: Dec 6 2022Dec 9 2022

Publication series

NameProceedings - SIGGRAPH Asia 2022 Conference Papers

Conference

ConferenceSIGGRAPH Asia 2022 - Computer Graphics and Interactive Techniques Conference - Asia, SA 2022
Country/TerritoryKorea, Republic of
CityDaegu
Period12/6/2212/9/22

All Science Journal Classification (ASJC) codes

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

Keywords

  • indoor layout synthesis
  • interior design
  • neural networks

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