# House-GAN++: Generative Adversarial Layout Refinement Networks

arXiv: https://arxiv.org/abs/2103.02574 · HTML: https://mlsanity.com/paper/2103.02574/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 32 references that are in the map.
- First posted on arXiv 3 Mar 2021.

## Builds on (most cited first, 18 of 18)

- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models](https://mlsanity.com/paper/1802.08773/) (2018)
- [Image Generation from Scene Graphs](https://mlsanity.com/paper/1804.01622/) (2018)
- [Efficient Graph Generation with Graph Recurrent Attention Networks](https://mlsanity.com/paper/1910.00760/) (2019)
- [LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators](https://mlsanity.com/paper/1901.06767/) (2019)
- [LayoutVAE: Stochastic Scene Layout Generation From a Label Set](https://mlsanity.com/paper/1907.10719/) (2019)
- [Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models](https://mlsanity.com/paper/1811.12463/) (2018)
- [RoadTracer: Automatic Extraction of Road Networks from Aerial Images](https://mlsanity.com/paper/1802.03680/) (2018)
- [MaskGAN: Better Text Generation via Filling in the______](https://mlsanity.com/paper/1801.07736/) (2018)
- [House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation](https://mlsanity.com/paper/2003.06988/) (2020)
- [Specifying Object Attributes and Relations in Interactive Scene Generation](https://mlsanity.com/paper/1909.05379/) (2019)
- [Floor-SP: Inverse CAD for Floorplans by Sequential Room-wise Shortest Path](https://mlsanity.com/paper/1908.06702/) (2019)
- [Neural Turtle Graphics for Modeling City Road Layouts](https://mlsanity.com/paper/1910.02055/) (2019)
- [Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order](https://mlsanity.com/paper/2004.11579/) (2020)
- [Conv-MPN: Convolutional Message Passing Neural Network for Structured Outdoor Architecture Reconstruction](https://mlsanity.com/paper/1912.01756/) (2019)
- [GANHopper: Multi-Hop GAN for Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/2002.10102/) (2020)
- [Intelligent Home 3D: Automatic 3D-House Design from Linguistic Descriptions Only](https://mlsanity.com/paper/2003.00397/) (2020)

## Built on by (most cited first, 13 of 13)

- [Next Steps for Human-Centered Generative AI: A Technical Perspective](https://mlsanity.com/paper/2306.15774/) (2023)
- [iPLAN: Interactive and Procedural Layout Planning](https://mlsanity.com/paper/2203.14412/) (2022)
- [Vitruvio: 3D Building Meshes via Single Perspective Sketches](https://mlsanity.com/paper/2210.13634/) (2022)
- [DStruct2Design: Data and Benchmarks for Data Structure Driven Generative Floor Plan Design](https://mlsanity.com/paper/2407.15723/) (2024)
- [ReCo: A Dataset for Residential Community Layout Planning](https://mlsanity.com/paper/2206.04678/) (2022)
- [SSIG: A Visually-Guided Graph Edit Distance for Floor Plan Similarity](https://mlsanity.com/paper/2309.04357/) (2023)
- [Design Process is a Reinforcement Learning Problem](https://mlsanity.com/paper/2211.03136/) (2022)
- [Adapting HouseDiffusion for conditional Floor Plan generation on Modified Swiss Dwellings dataset](https://mlsanity.com/paper/2312.03938/) (2023)
- [Generating floorplans for various building functionalities via latent diffusion model](https://mlsanity.com/paper/2412.06859/) (2024)
- [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)
- [GFLAN: Generative Functional Layouts](https://mlsanity.com/paper/2512.16275/) (2025)
- [CoMa: Contextual Massing Generation with Vision-Language Models](https://mlsanity.com/paper/2601.08464/) (2026)
- [GRE-Diff: Gaussian Room Embeddings for Structured Layout Diffusion](https://mlsanity.com/paper/2607.08086/) (2026)

## Abstract (arXiv)

This paper proposes a novel generative adversarial layout refinement network for automated floorplan generation. Our architecture is an integration of a graph-constrained relational GAN and a conditional GAN, where a previously generated layout becomes the next input constraint, enabling iterative refinement. A surprising discovery of our research is that a simple non-iterative training process, dubbed component-wise GT-conditioning, is effective in learning such a generator. The iterative generator also creates a new opportunity in further improving a metric of choice via meta-optimization techniques by controlling when to pass which input constraints during iterative layout refinement. Our qualitative and quantitative evaluation based on the three standard metrics demonstrate that the proposed system makes significant improvements over the current state-of-the-art, even competitive against the ground-truth floorplans, designed by professional architects.