# Graph2Plan: Learning Floorplan Generation from Layout Graphs

arXiv: https://arxiv.org/abs/2004.13204 · HTML: https://mlsanity.com/paper/2004.13204/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2020.

## Built on by (most cited first, 15 of 15)

- [Holodeck: Language Guided Generation of 3D Embodied AI Environments](https://mlsanity.com/paper/2312.09067/) (2023)
- [HouseDiffusion: Vector Floorplan Generation via a Diffusion Model with Discrete and Continuous Denoising](https://mlsanity.com/paper/2211.13287/) (2022)
- [Graph Transformer GANs for Graph-Constrained House Generation](https://mlsanity.com/paper/2303.08225/) (2023)
- [WAFFLE: Multimodal Floorplan Understanding in the Wild](https://mlsanity.com/paper/2412.00955/) (2024)
- [ReCo: A Dataset for Residential Community Layout Planning](https://mlsanity.com/paper/2206.04678/) (2022)
- [AutoEncoding Tree for City Generation and Applications](https://mlsanity.com/paper/2309.15941/) (2023)
- [In-game Residential Home Planning via Visual Context-aware Global Relation Learning](https://mlsanity.com/paper/2102.04035/) (2021)
- [Generating Topological Structure of Floorplans from Room Attributes](https://mlsanity.com/paper/2204.12338/) (2022)
- [Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the Art](https://mlsanity.com/paper/2311.10129/) (2023)
- [Learning Aesthetic Layouts via Visual Guidance](https://mlsanity.com/paper/2107.06262/) (2021)
- [A Novel Framework for Automated Warehouse Layout Generation](https://mlsanity.com/paper/2407.08633/) (2024)
- [ArchShapeNet:An Interpretable 3D-CNN Framework for Evaluating Architectural Shapes](https://mlsanity.com/paper/2506.14832/) (2025)
- [Fluid Grey 2: How Well Does Generative Adversarial Network Learn Deeper Topology Structure in Architecture That Matches Images?](https://mlsanity.com/paper/2511.17643/) (2025)
- [CoMa: Contextual Massing Generation with Vision-Language Models](https://mlsanity.com/paper/2601.08464/) (2026)
- [TLC-Plan: A Two-Level Codebook Based Network for End-to-End Vector Floorplan Generation](https://mlsanity.com/paper/2602.07100/) (2026)

## Abstract (arXiv)

We introduce a learning framework for automated floorplan generation which combines generative modeling using deep neural networks and user-in-the-loop designs to enable human users to provide sparse design constraints. Such constraints are represented by a layout graph. The core component of our learning framework is a deep neural network, Graph2Plan, which converts a layout graph, along with a building boundary, into a floorplan that fulfills both the layout and boundary constraints. Given an input building boundary, we allow a user to specify room counts and other layout constraints, which are used to retrieve a set of floorplans, with their associated layout graphs, from a database. For each retrieved layout graph, along with the input boundary, Graph2Plan first generates a corresponding raster floorplan image, and then a refined set of boxes representing the rooms. Graph2Plan is trained on RPLAN, a large-scale dataset consisting of 80K annotated floorplans. The network is mainly based on convolutional processing over both the layout graph, via a graph neural network (GNN), and the input building boundary, as well as the raster floorplan images, via conventional image convolution.