# GFLAN: Generative Functional Layouts

arXiv: https://arxiv.org/abs/2512.16275 · HTML: https://mlsanity.com/paper/2512.16275/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 50 references that are in the map.
- First posted on arXiv 18 Dec 2025.

## Builds on (most cited first, 8 of 8)

- [House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation](https://mlsanity.com/paper/2003.06988/) (2020)
- [HouseDiffusion: Vector Floorplan Generation via a Diffusion Model with Discrete and Continuous Denoising](https://mlsanity.com/paper/2211.13287/) (2022)
- [Generative Layout Modeling using Constraint Graphs](https://mlsanity.com/paper/2011.13417/) (2020)
- [Graph Transformer GANs for Graph-Constrained House Generation](https://mlsanity.com/paper/2303.08225/) (2023)
- [House-GAN++: Generative Adversarial Layout Refinement Networks](https://mlsanity.com/paper/2103.02574/) (2021)
- [ActFloor-GAN: Activity-Guided Adversarial Networks for Human-Centric Floorplan Design](https://mlsanity.com/paper/2111.03545/) (2021)
- [ResPlan: A Large-Scale Vector-Graph Dataset of 17,000 Residential Floor Plans](https://mlsanity.com/paper/2508.14006/) (2025)
- [Constrained Layout Generation with Factor Graphs](https://mlsanity.com/paper/2404.00385/) (2024)

## Abstract (arXiv)

Automated floor plan generation lies at the intersection of combinatorial search, geometric constraint satisfaction, and functional design requirements -- a confluence that has historically resisted a unified computational treatment. While recent deep learning approaches have improved the state of the art, they often struggle to capture architectural reasoning: the precedence of topological relationships over geometric instantiation, the propagation of functional constraints through adjacency networks, and the emergence of circulation patterns from local connectivity decisions. To address these fundamental challenges, this paper introduces GFLAN, a generative framework that restructures floor plan synthesis through explicit factorization into topological planning and geometric realization. Given a single exterior boundary and a front-door location, our approach departs from direct pixel-to-pixel or wall-tracing generation in favor of a principled two-stage decomposition. Stage A employs a specialized convolutional architecture with dual encoders -- separating invariant spatial context from evolving layout state -- to sequentially allocate room centroids within the building envelope via discrete probability maps over feasible placements. Stage B constructs a heterogeneous graph linking room nodes to boundary vertices, then applies a Transformer-augmented graph neural network (GNN) that jointly regresses room boundaries.