# Fast and Scalable Position-Based Layout Synthesis

arXiv: https://arxiv.org/abs/1809.10526 · HTML: https://mlsanity.com/paper/1809.10526/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2018.

## Built on by (most cited first, 16 of 16)

- [I-Design: Personalized LLM Interior Designer](https://mlsanity.com/paper/2404.02838/) (2024)
- [LEGO-Net: Learning Regular Rearrangements of Objects in Rooms](https://mlsanity.com/paper/2301.09629/) (2023)
- [FlairGPT: Repurposing LLMs for Interior Designs](https://mlsanity.com/paper/2501.04648/) (2025)
- [LayoutEnhancer: Generating Good Indoor Layouts from Imperfect Data](https://mlsanity.com/paper/2202.00185/) (2022)
- [COFS: Controllable Furniture layout Synthesis](https://mlsanity.com/paper/2205.14657/) (2022)
- [CasaGPT: Cuboid Arrangement and Scene Assembly for Interior Design](https://mlsanity.com/paper/2504.19478/) (2025)
- [Forest2Seq: Revitalizing Order Prior for Sequential Indoor Scene Synthesis](https://mlsanity.com/paper/2407.05388/) (2024)
- [ReSpace: Text-Driven Autoregressive 3D Indoor Scene Synthesis and Editing](https://mlsanity.com/paper/2506.02459/) (2025)
- [Co-Layout: LLM-driven Co-optimization for Interior Layout](https://mlsanity.com/paper/2511.12474/) (2025)
- [CasLayout: Cascaded 3D Layout Diffusion for Indoor Scene Synthesis with Implicit Relation Modeling](https://mlsanity.com/paper/2604.27361/) (2026)
- [Style Similarity as Feedback for Product Design](https://mlsanity.com/paper/2105.12256/) (2021)
- [HierRelTriple: Guiding Indoor Layout Generation with Hierarchical Relationship Triplet Losses](https://mlsanity.com/paper/2503.20289/) (2025)
- [FilmSceneDesigner: Chaining Set Design for Procedural Film Scene Generation](https://mlsanity.com/paper/2511.19137/) (2025)
- [Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments](https://mlsanity.com/paper/2607.02407/) (2026)
- [Learning an Interior Layout Policy in a Domain Specific Language Action Space](https://mlsanity.com/paper/2608.07547/) (2026)
- [Co-DSL: Human-LLM Co-design of Reusable Spatial Concepts for Layout Generation](https://mlsanity.com/paper/2610.15290/) (2026)

## Abstract (arXiv)

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.