Graph-RWGAN: A Method for Generating House Layouts Based on Multi-Relation Graph Attention Mechanism
Abstract
1. Introduction
- Multi-relational graph attention mechanism: For the first time, multi-relational graph attention is introduced into house layout generation to capture the complex spatial and functional relationships between room nodes and improve the rationality of room layout.
- Iterated prediction generator: Through multiple iterations, the node features and room relationships are gradually optimized to achieve accurate generation of the layout. It supports iterative modification and dynamic optimization to make up for the shortcomings of traditional methods that cannot adjust the layout.
- Conditional graph discriminator combined with Wasserstein loss: A conditional graph discriminator with Wasserstein loss enforces global consistency in generated layouts, ensuring realistic spatial connectivity and rationality while enhancing training stability and layout diversity.
- Efficient layout generation under weak constraints: The proposed method generates house layouts under partial constraints with flexibility, controllability, and strong adaptability, while enhancing style diversity to provide rich solutions for diverse building types and personalized design needs.
2. Related Work
2.1. Graph Attention Module
2.2. Generators in Different Domains
2.3. Boundary-Aware Graph Convolution Module
2.4. Conditional Graph Discriminator
3. Preparatory Work
3.1. Captures the Relationship Between Room Nodes
3.2. Iterative Prediction Generator for Complete Room Graph Inference
3.3. Conditional Graph Discriminator with Wasserstein Loss
4. Methodology
4.1. Graph-RWGAN Network Framework
4.2. Relationship Prediction Module
4.3. Multi-Relation Graph Attention Module
4.4. Layout Generation Module
4.5. Conditional Graph Discriminator with Wasserstein Loss
4.6. User Input and Weak Constraint Specification
5. Experimental Setup
5.1. Dataset Introduction
5.2. Experimental Environment
5.3. Evaluation Indicators
6. Evaluation and Analysis
6.1. Preprocessing of Data
- Door connections: These edges represent rooms that are directly accessible via a door, allowing for movement or functional interaction between the spaces.
- Wall connections: These edges signify rooms that share a common wall, indicating a physical separation that prevents direct spatial flow between them but still establishes a structural connection.
- General connections: This category encompasses both door and wall connections, representing broader neighborhood relationships between rooms that might not necessarily require direct access but still form part of the layout’s overall connectivity.
6.2. Experimental Results
6.3. Ablation Study
- Iterative Learning Mechanism: This mechanism allows the model to progressively improve its predictions over time. By disabling this component, we observe a significant decrease in the model’s ability to predict unknown relationships, as reflected in lower accuracy and poorer layout generation quality.
- Relation Prediction Module: This module plays a crucial role in predicting spatial relationships between rooms. Without it, the model’s accuracy decreases, indicating the importance of precise relationship prediction for generating realistic and coherent layouts.
- Multi-Relation Graph Attention Module: This module consolidates information from multiple edge relationship types, improving the model’s capacity to capture intricate spatial dependencies. Its removal results in noticeable performance drops, especially in terms of structural consistency and the accuracy of room topology.
6.4. Runtime and Efficiency Evaluation
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Model | FID ↓ | SSIM ↑ | Acc (%) ↑ | IS ↑ | Room Adjacency Score ↑ |
---|---|---|---|---|---|
House-GAN [29] | 109.27 | 0.784 | 81.34 | 2.85 | 0.71 |
LayoutGAN [30] | 175.36 | 0.525 | 68.22 | 2.12 | 0.60 |
MR-GAT [31] | 94.69 | 0.813 | 82.37 | 3.05 | 0.74 |
Graph-RWGAN | 92.73 | 0.828 | 85.96 | 3.12 | 0.78 |
Model | Room Topology Accuracy (%) | Layout Quality (%) | Structural Coherence (%) |
---|---|---|---|
House-GAN | 60% | 70% | 65% |
LayoutGAN | 45% | 50% | 55% |
MR-GAT | 75% | 85% | 80% |
Graph-RWGAN | 95% | 90% | 95% |
Method | IoU (%) | Position Error | Structural Consistency | Visual Realism |
---|---|---|---|---|
MA-GAN | 62.3 | 15.8 | 0.68 | 0.72 |
MR-GAT | 68.5 | 12.5 | 0.75 | 0.78 |
House-GAN | 71.2 | 10.3 | 0.80 | 0.82 |
LayoutDiffusion | 83.0 | 6.0 | 0.90 | 0.96 |
Our Method | 85.6 | 5.2 | 0.92 | 0.95 |
Model Variant | FID ↓ | SSIM ↑ | Acc ↑ |
---|---|---|---|
w/o Iterative Learning | 112.87 | 0.729 | 74.17 |
w/o Relation Prediction Module | 98.49 | 0.752 | 72.67 |
w/o Multi-Relation Graph Attention | 109.15 | 0.693 | 69.29 |
Ours (Graph-RWGAN) | 92.73 | 0.828 | 85.96 |
Model | Generation Time (s) | Memory Consumption (GB) | Inference Time (s) | Time for 1000 Layouts (min) |
---|---|---|---|---|
Graph-RWGAN | 1.2 | 2.1 | 0.8 | 20 |
MA-GAN | 1.8 | 2.3 | 1.0 | 25 |
MR-GAT | 2.0 | 2.5 | 1.2 | 28 |
House-GAN | 2.4 | 3.5 | 1.6 | 30 |
LayoutGAN | 3.1 | 4.2 | 2.0 | 40 |
LayoutDiffusion | 1.5 | 2.0 | 0.9 | 22 |
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Ye, Z.; Liu, S.; Tian, Z.; Chen, Y.; Zheng, L.; Chen, J. Graph-RWGAN: A Method for Generating House Layouts Based on Multi-Relation Graph Attention Mechanism. Buildings 2025, 15, 3623. https://doi.org/10.3390/buildings15193623
Ye Z, Liu S, Tian Z, Chen Y, Zheng L, Chen J. Graph-RWGAN: A Method for Generating House Layouts Based on Multi-Relation Graph Attention Mechanism. Buildings. 2025; 15(19):3623. https://doi.org/10.3390/buildings15193623
Chicago/Turabian StyleYe, Ziqi, Sirui Liu, Zhen Tian, Yile Chen, Liang Zheng, and Junming Chen. 2025. "Graph-RWGAN: A Method for Generating House Layouts Based on Multi-Relation Graph Attention Mechanism" Buildings 15, no. 19: 3623. https://doi.org/10.3390/buildings15193623
APA StyleYe, Z., Liu, S., Tian, Z., Chen, Y., Zheng, L., & Chen, J. (2025). Graph-RWGAN: A Method for Generating House Layouts Based on Multi-Relation Graph Attention Mechanism. Buildings, 15(19), 3623. https://doi.org/10.3390/buildings15193623