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

arXiv: https://arxiv.org/abs/2508.11177 · HTML: https://mlsanity.com/paper/2508.11177/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2025.

## Built on by (most cited first, 3 of 3)

- [APEX: Academic Poster Editing Agentic Expert](https://mlsanity.com/paper/2601.04794/) (2026)
- [LLMs as Layout Designers: Enhanced Spatial Reasoning for Content-Aware Layout Generation](https://mlsanity.com/paper/2509.16891/) (2025)
- [From Pixels to Policies: Reinforcing Spatial Reasoning in Language Models for Content-Aware Layout Design](https://mlsanity.com/paper/2602.13912/) (2026)

## Abstract (arXiv)

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.