# House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation

arXiv: https://arxiv.org/abs/2003.06988 · HTML: https://mlsanity.com/paper/2003.06988/

## Key facts

- Cited by 87 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2020.

## Built on by (most cited first, 30 of 87)

- [AnyHome: Open-Vocabulary Generation of Structured and Textured 3D Homes](https://mlsanity.com/paper/2312.06644/) (2023)
- [UltraLiDAR: Learning Compact Representations for LiDAR Completion and Generation](https://mlsanity.com/paper/2311.01448/) (2023)
- [HouseDiffusion: Vector Floorplan Generation via a Diffusion Model with Discrete and Continuous Denoising](https://mlsanity.com/paper/2211.13287/) (2022)
- [Learning to Compose Visual Relations](https://mlsanity.com/paper/2111.09297/) (2021)
- [SINGAPO: Single Image Controlled Generation of Articulated Parts in Objects](https://mlsanity.com/paper/2410.16499/) (2024)
- [SketchGen: Generating Constrained CAD Sketches](https://mlsanity.com/paper/2106.02711/) (2021)
- [Engineering Sketch Generation for Computer-Aided Design](https://mlsanity.com/paper/2104.09621/) (2021)
- [SceneHGN: Hierarchical Graph Networks for 3D Indoor Scene Generation with Fine-Grained Geometry](https://mlsanity.com/paper/2302.10237/) (2023)
- [Frankenstein: Generating Semantic-Compositional 3D Scenes in One Tri-Plane](https://mlsanity.com/paper/2403.16210/) (2024)
- [PLay: Parametrically Conditioned Layout Generation using Latent Diffusion](https://mlsanity.com/paper/2301.11529/) (2023)
- [Dolfin: Diffusion Layout Transformers without Autoencoder](https://mlsanity.com/paper/2310.16305/) (2023)
- [Building-GAN: Graph-Conditioned Architectural Volumetric Design Generation](https://mlsanity.com/paper/2104.13316/) (2021)
- [Graph Transformer GANs for Graph-Constrained House Generation](https://mlsanity.com/paper/2303.08225/) (2023)
- [DoodleFormer: Creative Sketch Drawing with Transformers](https://mlsanity.com/paper/2112.03258/) (2021)
- [Exploring Challenges and Opportunities to Support Designers in Learning to Co-create with AI-based Manufacturing Design Tools](https://mlsanity.com/paper/2303.00192/) (2023)
- [LayoutDETR: Detection Transformer Is a Good Multimodal Layout Designer](https://mlsanity.com/paper/2212.09877/) (2022)
- [GlobalMapper: Arbitrary-Shaped Urban Layout Generation](https://mlsanity.com/paper/2307.09693/) (2023)
- [MSD: A Benchmark Dataset for Floor Plan Generation of Building Complexes](https://mlsanity.com/paper/2407.10121/) (2024)
- [House-GAN++: Generative Adversarial Layout Refinement Networks](https://mlsanity.com/paper/2103.02574/) (2021)
- [Generative AI Models for Different Steps in Architectural Design: A Literature Review](https://mlsanity.com/paper/2404.01335/) (2024)
- [Automated Urban Planning for Reimagining City Configuration via Adversarial Learning: Quantification, Generation, and Evaluation](https://mlsanity.com/paper/2112.14699/) (2021)
- [X-GGM: Graph Generative Modeling for Out-of-Distribution Generalization in Visual Question Answering](https://mlsanity.com/paper/2107.11576/) (2021)
- [COHO: Context-Sensitive City-Scale Hierarchical Urban Layout Generation](https://mlsanity.com/paper/2407.11294/) (2024)
- [Conditional 360-degree Image Synthesis for Immersive Indoor Scene Decoration](https://mlsanity.com/paper/2307.09621/) (2023)
- [iPLAN: Interactive and Procedural Layout Planning](https://mlsanity.com/paper/2203.14412/) (2022)
- [Generative AIBIM: An automatic and intelligent structural design pipeline integrating BIM and generative AI](https://mlsanity.com/paper/2311.04052/) (2023)
- [Generating 3D House Wireframes with Semantics](https://mlsanity.com/paper/2407.12267/) (2024)
- [Aligning Constraint Generation with Design Intent in Parametric CAD](https://mlsanity.com/paper/2504.13178/) (2025)
- [Constrained Layout Generation with Factor Graphs](https://mlsanity.com/paper/2404.00385/) (2024)
- [Room Classification on Floor Plan Graphs using Graph Neural Networks](https://mlsanity.com/paper/2108.05947/) (2021)

## Abstract (arXiv)

This paper proposes a novel graph-constrained generative adversarial network, whose generator and discriminator are built upon relational architecture. The main idea is to encode the constraint into the graph structure of its relational networks. We have demonstrated the proposed architecture for a new house layout generation problem, whose task is to take an architectural constraint as a graph (i.e., the number and types of rooms with their spatial adjacency) and produce a set of axis-aligned bounding boxes of rooms. We measure the quality of generated house layouts with the three metrics: the realism, the diversity, and the compatibility with the input graph constraint. Our qualitative and quantitative evaluations over 117,000 real floorplan images demonstrate that the proposed approach outperforms existing methods and baselines. We will publicly share all our code and data.