# Building-GAN: Graph-Conditioned Architectural Volumetric Design Generation

arXiv: https://arxiv.org/abs/2104.13316 · HTML: https://mlsanity.com/paper/2104.13316/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 41 references that are in the map.
- First posted on arXiv 27 Apr 2021.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [3D Semantic Segmentation with Submanifold Sparse Convolutional Networks](https://mlsanity.com/paper/1711.10275/) (2017)
- [Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling](https://mlsanity.com/paper/1610.07584/) (2016)
- [Point-Voxel CNN for Efficient 3D Deep Learning](https://mlsanity.com/paper/1907.03739/) (2019)
- [OctNet: Learning Deep 3D Representations at High Resolutions](https://mlsanity.com/paper/1611.05009/) (2016)
- [PolyGen: An Autoregressive Generative Model of 3D Meshes](https://mlsanity.com/paper/2002.10880/) (2020)
- [Submanifold Sparse Convolutional Networks](https://mlsanity.com/paper/1706.01307/) (2017)
- [BSP-Net: Generating Compact Meshes via Binary Space Partitioning](https://mlsanity.com/paper/1911.06971/) (2019)
- [House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation](https://mlsanity.com/paper/2003.06988/) (2020)
- [Improved Adversarial Systems for 3D Object Generation and Reconstruction](https://mlsanity.com/paper/1707.09557/) (2017)
- [Learning Descriptor Networks for 3D Shape Synthesis and Analysis](https://mlsanity.com/paper/1804.00586/) (2018)
- [Generative Layout Modeling using Constraint Graphs](https://mlsanity.com/paper/2011.13417/) (2020)
- [Conv-MPN: Convolutional Message Passing Neural Network for Structured Outdoor Architecture Reconstruction](https://mlsanity.com/paper/1912.01756/) (2019)
- [General 3D Room Layout from a Single View by Render-and-Compare](https://mlsanity.com/paper/2001.02149/) (2020)
- [LayoutGMN: Neural Graph Matching for Structural Layout Similarity](https://mlsanity.com/paper/2012.06547/) (2020)
- [Thinking Outside the Box: Generation of Unconstrained 3D Room Layouts](https://mlsanity.com/paper/1905.03105/) (2019)
- [Learning to simulate and design for structural engineering](https://mlsanity.com/paper/2003.09103/) (2020)
- [End-to-end Generative Floor-plan and Layout with Attributes and Relation Graph](https://mlsanity.com/paper/2012.08514/) (2020)

## Built on by (most cited first, 20 of 20)

- [PLay: Parametrically Conditioned Layout Generation using Latent Diffusion](https://mlsanity.com/paper/2301.11529/) (2023)
- [3D Scene Generation: A Survey](https://mlsanity.com/paper/2505.05474/) (2025)
- [GlobalMapper: Arbitrary-Shaped Urban Layout Generation](https://mlsanity.com/paper/2307.09693/) (2023)
- [COHO: Context-Sensitive City-Scale Hierarchical Urban Layout Generation](https://mlsanity.com/paper/2407.11294/) (2024)
- [DreamView: Injecting View-specific Text Guidance into Text-to-3D Generation](https://mlsanity.com/paper/2404.06119/) (2024)
- [Constrained Layout Generation with Factor Graphs](https://mlsanity.com/paper/2404.00385/) (2024)
- [Leveraging Generative AI for Urban Digital Twins: A Scoping Review on the Autonomous Generation of Urban Data, Scenarios, Designs, and 3D City Models for Smart City Advancement](https://mlsanity.com/paper/2405.19464/) (2024)
- [Vitruvio: 3D Building Meshes via Single Perspective Sketches](https://mlsanity.com/paper/2210.13634/) (2022)
- [Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation](https://mlsanity.com/paper/2401.07721/) (2024)
- [AutoEncoding Tree for City Generation and Applications](https://mlsanity.com/paper/2309.15941/) (2023)
- [Computer-Aided Layout Generation for Building Design: A Review](https://mlsanity.com/paper/2504.09694/) (2025)
- [Deep Sketch-Based 3D Modeling: A Survey](https://mlsanity.com/paper/2603.03287/) (2026)
- [Automating Computational Design with Generative AI](https://mlsanity.com/paper/2307.02511/) (2023)
- [Representation Learning for Sequential Volumetric Design Tasks](https://mlsanity.com/paper/2309.02583/) (2023)
- [RoBus: A Multimodal Dataset for Controllable Road Networks and Building Layouts Generation](https://mlsanity.com/paper/2407.07835/) (2024)
- [ShellMaker: Language-Guided Exterior Completion under Structural Constraints](https://mlsanity.com/paper/2606.31680/) (2026)
- [Integrating Symbolic Reasoning into Neural Generative Models for Design Generation](https://mlsanity.com/paper/2310.09383/) (2023)
- [CGGM: A conditional graph generation model with adaptive sparsity for node anomaly detection in IoT networks](https://mlsanity.com/paper/2402.17363/) (2024)
- [ArchComplete: Autoregressive 3D Architectural Design Generation with Hierarchical Diffusion-Based Upsampling](https://mlsanity.com/paper/2412.17957/) (2024)
- [ArchShapeNet:An Interpretable 3D-CNN Framework for Evaluating Architectural Shapes](https://mlsanity.com/paper/2506.14832/) (2025)

## Abstract (arXiv)

Volumetric design is the first and critical step for professional building design, where architects not only depict the rough 3D geometry of the building but also specify the programs to form a 2D layout on each floor. Though 2D layout generation for a single story has been widely studied, there is no developed method for multi-story buildings. This paper focuses on volumetric design generation conditioned on an input program graph. Instead of outputting dense 3D voxels, we propose a new 3D representation named voxel graph that is both compact and expressive for building geometries. Our generator is a cross-modal graph neural network that uses a pointer mechanism to connect the input program graph and the output voxel graph, and the whole pipeline is trained using the adversarial framework. The generated designs are evaluated qualitatively by a user study and quantitatively using three metrics: quality, diversity, and connectivity accuracy. We show that our model generates realistic 3D volumetric designs and outperforms previous methods and baselines.