LayoutRectifier: An Optimization-based Post-processing for Graphic Design Layout Generation

TL;DR

LayoutRectifier is an optimization-based post-processing technique that improves automatically generated graphic design layouts by reducing flaws like misalignment and overlaps, using a two-stage process involving grid systems and containment functions.

Contribution

It introduces a novel two-stage optimization method that rectifies generated layouts without additional training, enhancing their quality for design applications.

Findings

Achieves better layout quality in content-agnostic and content-aware tasks.

Reduces misalignments, overlaps, and containment issues effectively.

Does not require extra training, complementing existing deep learning methods.

Abstract

Recent deep learning methods can generate diverse graphic design layouts efficiently. However, these methods often create layouts with flaws, such as misalignment, unwanted overlaps, and unsatisfied containment. To tackle this issue, we propose an optimization-based method called LayoutRectifier, which gracefully rectifies auto-generated graphic design layouts to reduce these flaws while minimizing deviation from the generated layout. The core of our method is a two-stage optimization. First, we utilize grid systems, which professional designers commonly use to organize elements, to mitigate misalignments through discrete search. Second, we introduce a novel box containment function designed to adjust the positions and sizes of the layout elements, preventing unwanted overlapping and promoting desired containment. We evaluate our method on content-agnostic and content-aware layout generation tasks and achieve better-quality layouts that are more suitable for downstream graphic design tasks. Our method complements learning-based layout generation methods and does not require additional training.