Fast and Scalable Position-Based Layout Synthesis

Tomer Weiss, Alan Litteneker, Noah Duncan, Masaki Nakada, Chenfanfu Jiang, Lap Fai Yu, Demetri Terzopoulos

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


The arrangement of objects into a layout can be challenging for non-experts, as is affirmed by the existence of interior design professionals. Recent research into the automation of this task has yielded methods that can synthesize layouts of objects respecting aesthetic and functional constraints that are non-linear and competing. These methods usually adopt a stochastic optimization scheme, which samples from different layout configurations, a process that is slow and inefficient. We introduce an physics-motivated, continuous layout synthesis technique, which results in a significant gain in speed and is readily scalable. We demonstrate our method on a variety of examples and show that it achieves results similar to conventional layout synthesis based on Markov chain Monte Carlo (McMC) state-search, but is faster by at least an order of magnitude and can handle layouts of unprecedented size as well as tightly-packed layouts that can overwhelm McMC.

Original languageEnglish (US)
Article number8443151
Pages (from-to)3231-3243
Number of pages13
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number12
StatePublished - Dec 1 2019
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


  • 3D scene modeling
  • Automatic layout synthesis
  • automatic content creation
  • constraints
  • position-based methods


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