  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.
