Fast and Scalable Position-Based Layout Synthesis

TL;DR

This paper presents a physics-inspired, continuous approach to layout synthesis that significantly improves speed and scalability over traditional stochastic methods, enabling efficient creation of large and tightly-packed object arrangements.

Contribution

A novel physics-motivated, continuous layout synthesis method that outperforms stochastic optimization in speed and scalability, suitable for complex and large-scale layouts.

Findings

Achieves at least ten times faster results than McMC-based methods.

Handles larger and more tightly-packed layouts effectively.

Maintains similar quality of layout results compared to traditional methods.

Abstract

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.