House Spatial Layout Generation via Generative Adversarial Network

BARC0141: Built Environment Dissertation

House Spatial Layout Generation via Generative Adversarial Networks

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Name: Yue Yang Student number: 22211877 Date: 03/09/2023 Word count: 8520 Dissertation submitted in part fulfilment of the Degree of Master MSc Architectural Computation Bartlett School Architecture University College London

UCL FACULTY OF THE BUILT ENVIRONMENT BARTLETT SCHOOL OF GRADUATE STUDIES

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Name: Yue Yang Signed: Date: 03/09/2023

CONTENTS List of Figures Abstract Acknowledgements 1. Introduction 1.1 Background 1.2 Problem Statement 1.3 Research Aim 2. Literature Review 2.1 Evolution and Variants of GAN 2.2 Evaluation of open source GAN model 2.3 Network Structure Optimization 2.4 Related works 2.5 Contribution 2.6 Systematic Layout Planning (SLP) 3. Methodology and workflow 3.1 The production of datasets and the web framework 3.1.1 Controllable Parameters 3.1.2 Dataset Creation 3.1.3 Data Processing 3.1.4 Network Structure 3.1.5 Network Training 3.2 Workflow 3.2.1

Defining Architectural Boundaries

3.2.2

Image Input Creation

3.2.3

Image Pairing Techniques

3.2.4

Iterative Process of Architectural Image Generation

3.2.5

Design Evaluation and Continuous Optimization

4. Result 4.1 Compare two generations of different GAN 4.2 For tests on known architectural boundaries and internal openings 4.3 Generating images with fixed internal walls 4.4 Generating architectural floor plans after fixing specific functional areas 5. Discussion 5.1 Analysis and Interpretation of Results 5.2 Significance of the Findings 5.3 Limitations of the Research 6. Conclusion

6.1 Initial Aim 6.2 Key Points and Results 6.3 Future Work 7. References

List of Figures Figure 1: Testing image pair in each training epoch for the model (Hao, Zheng et al.,2020) Figure 2: Sample bubble diagrams and house layouts in our dataset (Nauata et al., 2018) Figure 3: OT facility layout planning procedure (Lin et al. 2015) Figure 4: Catalogue image of the dataset created Figure 5: Color correspondence to different function areas Figure 6: Testing Image Pair with boundary and elevation entrance. Left existing floor plan; Middle boundary plan; Right function area plan.

Figure 7: Testing Image Pair with boundary and internal wall. Left existing floor plan; Middle boundary plan; Right function area plan.

Figure 8: Testing Image Pair with specifying the position of functional zones. Left existing floor plan; Middle boundary plan; Right function area plan.

Figure 9: Multi-unit building function area plan Figure 10: Multi-story building function area plan.

Figure 11: Diagram of GAN (Generative Adversarial Networks – Hot Topic in Machine Learning n.d.)

Figure 12: Training data Figure 13: Images generated during training.

Figure 14: Diagram of Workflow.

Figure 15: Testing data with boundary and elevation entrance. Left: existing building plan; Right: boundary plan input.

Figure 16: Testing data with boundary and elevation entrance. Left: existing building plan; Right: boundary plan input.

Figure 17: Generation result without optimization – boundary and elevation entrance.

Figure 18: Generation with optimization - boundary and elevation entrance.

Figure 19: Generation without optimization - boundary and internal wall.

Figure 20: Generation with optimization - boundary and internal wall/ Figure 21: Generation without optimization - boundary and specifying the position of functional zones.

Figure 22: Generation with optimization - boundary and specifying the position of functional zones/ Figure 23: Generation result for Multi-unit building function area plan.

Figure 24: Generation result for Multi-story building with boundary and elevation entrance.

Figure 25: Generation result for Multi-story building with boundary and toilet/stair position of functional zones.

ACKNOWLEDGEMENTS I would like to put forward my gratitude to my dissertation supervisor, Vishu Bhooshan, for guiding me over the past few months and taking time out of his busy schedule to help me. Last but not least, I would like to thank my technical supervisor Marcin Kosicki for providing me with excellent technical guidance and module explanations.

ABSTRACT In the rapidly evolving domain of architectural design, the fusion of traditional methodologies with cutting-edge technology has become increasingly significant. This dissertation delves deep into this synergy, exploring the transformative potential of Generative Adversarial Networks (GAN) (Goodfellow et al., 2014) in the realm of architectural floor plan generation.

Central to this research is the pix2pix network model, a renowned architecture within the GAN landscape. Recognizing its foundational importance, the study introduces pivotal enhancements to this model. A novel self-attention mechanism is integrated within the generator, facilitating a more sophisticated understanding and representation of spatial relationships in architectural designs.

Simultaneously, the introduction of spectral normalization within the discriminator ensures a stabilized generation process, leading to outputs of superior quality.

The research ambitiously expands the horizons of GAN applications in architecture. Traditional design paradigms, often confined to established templates, are challenged by the versatility of the enhanced GAN model. This model is adept at generating intricate floor plans, catering to diverse architectural needs, from multi-unit residences to expansive multi-storey structures. Such versatility is poised to redefine architectural design, offering professionals a dynamic and adaptive tool tailored to contemporary urban demands.

Challenges inherent to the application of GAN in architectural design are not overlooked. The dissertation delves into issues like model stability, the intricacies of detail enhancement, and the challenges associated with generating non-standard room shapes. Each challenge is dissected, with the research shedding light on potential avenues for resolution, laying the groundwork for future endeavours in this domain.

Keywords: Deep Learning, GAN, Building Floor Plan Generation, Self-Attention, spectral normalization, Multi-unit Building.

1. INTRODUCTION

1.1 Background Architectural floor plans have a rich history that dates back to ancient civilizations. The representation of architectural design through floor plans can be traced to the Egyptians and Mesopotamians, who depicted simple layouts of buildings on materials like papyrus or clay tablets (Yu, 1998). These early floor plans mainly focused on temples, palaces, and residential structures.

As human civilization progressed, the art of architectural floor plans continued to evolve. During the Renaissance, there was a significant revival of interest in classical architecture, and more detailed floor plans began to be used in architectural design (Chen, 1997). The use of formal architectural drawings became more prevalent, and the representation of buildings on paper became more sophisticated (Alberti, 1988).

However, it wasn't until the emergence of computer-aided design (CAD) that the way architects and designers worked underwent a transformative shift. With the introduction of CAD technology, handdrawn plans gradually gave way to digital drafting methods (Carpo, 2017). Architects and designers could now use computer software to create precise and detailed representations of architectural plans.

This shift greatly improved efficiency, accuracy, and the ability to make revisions to designs.

Over time, CAD software has become increasingly sophisticated, offering advanced tools and features that streamline the design process. 3D modelling capabilities allow architects to create realistic renderings of buildings, enabling clients and stakeholders to better visualize the final product. Collaboration among design teams has also improved as digital plans can be easily shared and modified.

Today, computer-aided design is a fundamental tool in architectural practice. It has become an indispensable part of the design process, from initial conceptualization to construction documentation. While traditional hand-drawn plans still hold artistic value and are used in some specialized contexts, the widespread adoption of CAD has revolutionized the field of architectural design, making it more accessible, efficient, and adaptable to the complex demands of modern architecture.

1.2 Problem Statement Throughout the design process, the dynamic and malleable nature of floor plans necessitates a degree of adaptability. Unforeseen modifications to spatial boundaries may arise, dictated by shifting needs, changing regulations, or evolving aesthetic considerations. These unexpected

changes necessitate extensive reassessment and, in some cases, a complete revision of the entire interior layout.

Consequently, architects and designers are compelled to invest considerable time and effort into iterative sketching. This process, while integral to design development, can be resource-intensive.

A significant proportion of these iterative sketches may ultimately prove inappropriate or unviable due to modifications in boundary constraints or changes in project direction. In other cases, these sketches, while appropriate at the time, may become obsolete as project parameters evolve.

This iterative and resource-intensive cycle poses a considerable challenge to designers. Not only does it demand substantial manpower and intellectual investment, but it can also result in significant time inefficiencies. The time spent sketching and revising could instead be used to explore innovative design solutions, engage with clients, or refine the finer details of the design.

Moreover, this cycle can have a considerable impact on the overall efficiency and productivity of the design process. The constant iteration and revision can overshadow the broader objectives of the design process, potentially leading to a loss of focus on the original design intent. As such, this cycle can greatly hamper the designer's productivity and overshadow the overall efficiency of the interior design process.

Ultimately, this challenge calls for a more efficient and dynamic approach to architectural design— one that can more readily adapt to changing constraints and evolving project parameters. This is not merely a matter of improving the design process; it is about fundamentally changing how architects and designers approach their work, enabling them to create more effective and responsive designs.

1.3 Research Aim In the context of Advanced Intelligent Generative Computing (AIGC) within the era of Artificial Intelligence, our primary aim is to pioneer a transformative shift in the architectural design process.

We aspire to accomplish this by harnessing the power of innovative machine learning techniques, specifically with a focus on developing a tool that equips architects with efficient and creative design solutions at the very onset of their projects. This tool, we anticipate, will significantly alleviate the existing substantial workload borne by architects during the design process.

At the heart of our proposed solution lies the Generative Adversarial Network (GAN) (Goodfellow, 2014), a state-of-the-art technology renowned for its ability to learn from and generate real-world images, inclusive of architectural floor plans. With the leverage of GAN, we aim to empower computers to autonomously generate interior design layouts within pre-specified design boundaries.

This innovative approach will effectively transform input images depicting design boundaries and façade elements, such as windows and doors, into detailed, and functional interior designs.

Our ultimate objective is to furnish designers with an intuitive tool that is versatile enough to handle single-unit, multi-unit, and multi-story building designs. For single-unit floor plans, the designer

will have the liberty to select the design boundaries, façade elements, and interior wall locations.

For more complex multi-unit or multi-story buildings, the designer can choose to stabilize the position of specific functional areas in the building plan, thereby facilitating logistical aspects such as pipework within the building. The tool will then generate a comprehensive interior design that conforms to the defined constraints. The designer can subsequently modify the output floor plan, achieving a high-quality and compliant design.

Through this approach, we anticipate streamlining the interior design process and bestowing architects with a robust, time-saving tool that simultaneously encourages creativity and enhances the overall quality of the design. Our project, thus, presents a blend of machine learning and architectural expertise with the potential to fuel innovation and revolutionize the traditional design process. By doing so, we aspire to reshape the contemporary landscape of architectural design and instigate a paradigm shift in the way designers operate.

2. LITERATURE REVIEW

2.1 Evolution and Variants of GAN Since the inception of Generative Adversarial Networks (GAN) (Goodfellow et.al, 2014), there have been significant transformations in various research domains. It is a method of unsupervised learning, where learning takes place through two neural networks competing against each other which is the generator and discriminator. These transformations can be categorized into structural adjustments, theoretical deepening, and application expansions. In the original GAN model (Goodfellow et al., 2014), minimal prior assumptions were made without specific constraints on data distribution. This design philosophy aimed to endow GAN with boundless modelling potential, ensuring that both the generator and discriminator could effectively collaborate under various data distributions. Moreover, GAN provided a revolutionary framework for unsupervised learning, no longer limited by human prior knowledge but allowing machine-to-machine "adversarial games" to reveal the intrinsic laws of the real world through extensive data training.

However, the strategy of pursuing boundless modelling capabilities poses a series of challenges.

Due to the high degree of freedom in the generation process, the stability and convergence of model training became significant issues, potentially leading to the so-called "mode collapse" phenomenon, halting the training process (Cao, 2017). Furthermore, the original GAN model faced challenges like gradient vanishing, increased training difficulty, misalignment between loss functions and training progress, lack of diversity in generated samples, and overfitting. Therefore, to overcome these challenges, numerous scholars have proposed various improved GAN models and training strategies, aiming to enhance model stability and improve the quality of generated results.

Among the myriad of GAN variants, the following four models are widely regarded as having milestone significance: Conditional Generative Adversarial Nets (CGANs) (Mirza et al., 2014). This model introduces a conditional variable c to add constraints to the generation process, thereby guiding data generation more specifically. This design allows the model to generate specific targets and may achieve faster convergence in some cases. However, it requires high-quality datasets with labels or pre-marked data. Thus, CGANs are particularly suitable for supervised learning or scenarios requiring specific target generation.

Deep Convolutional Generative Adversarial Networks (DCGANs) (Radford et al., 2016). DCGANs combine GAN with Convolutional Neural Networks (CNN), incorporating stride convolution, fractional-stride convolution, batch normalization, and LRELU techniques. This structural optimization stabilizes the training process and enhances the diversity of generated samples.

However, parameter adjustments might be necessary for different datasets.

Wasserstein Generative Adversarial Networks (WGANs) (Arjovsky et al., 2018). The main

innovation of WGANs lies in the adoption of weight clipping, which theoretically addresses the gradient vanishing issue. However, improper weight clipping might introduce other gradient challenges.

Wasserstein Generative Adversarial Networks - Gradient Penalty (WGANs-GP) (Gulrajani et al., 2017). In comparison to (Arjovsky et. Al, 2018), this introduced a gradient penalty to replace weight clipping. This design stabilizes model training and allows for more direct text data processing.

However, it might lag slightly in terms of convergence speed and diversity of generated samples.

2.2 Evaluation of open source GANs model In the field of image processing, there are several open-source Generative Adversarial Network (GAN) framework models. This article provides a brief overview of the following four models: pix2pix (Zhu et al., 2017). It is based on the Conditional Generative Adversarial Network (CGAN) model, where the generator adopts the U-NET architecture, and the discriminator utilizes the PatchGAN classifier. A notable feature of pix2pix is its ability to achieve pixel-level image transfer, producing highly realistic images. Additionally, due to a significant reduction in parameters, this model excels in training speed and efficiency. However, it has stringent dataset requirements, necessitating paired one-to-one datasets. The model is primarily used for style transfer between paired images, such as style conversion of maps and generation of real objects with contour diagrams.

CycleGAN (Zhu et al., 2017), CycleGAN employs cycle consistency to achieve self-constraint, thereby enhancing the quality of the generated images. Its main advantage is the relatively low dataset requirement, only needing images of two styles to achieve bidirectional style transfer.

However, compared to pix2pix, the quality of the images it produces might be slightly inferior.

Common applications include seasonal transitions and art style transfers.

DiscoGAN (Kim et al., 2017), the core idea of DiscoGAN is to discover cross-domain relationships through two distinct GAN, facilitating style transfer. Its significant advantage is the ability to achieve one-to-one bidirectional mapping, improving the quality of the generated images. However, similar to pix2pix, it has strict dataset requirements. The model is mainly applied for image generation between different domains, such as gender conversion and mutual conversions between bags and shoes.

DTN (Taigman et al., 2016), the DTN generator network combines an input function with a learning function and employs various composite loss functions. Its primary strength lies in producing facial emojis with high visual appeal, capturing more facial features. However, due to the asymmetry of the input function and the lower information content in the new source domain, the results might have certain limitations. Typical applications include the conversion of human photos to anime images.

2.3 Network Structure Optimization In the field of deep learning, Generative Adversarial Networks (GAN) have become an essential tool for image generation tasks. Specifically, pix2pix, as a conditional GAN, has excelled in image-to-image translation tasks. However, like all GANs, Pix2pix also faces some common challenges, such as model collapse and a preference for low frequencies.

The preference for low frequencies means that while GAN can often capture the overall structure of an image well, they might encounter issues in the details and edges. Such problems can lead to color mixing or blurring in certain areas of the generated image, as you mentioned with the red circled sections. The emergence of these issues can severely affect the overall quality and realism of the image.

To address these challenges, researchers have proposed various solutions: Spectral Normalization (Miyato et al., 2018): This method stabilizes the training process by normalizing the weights of the discriminator (D). Experiments have shown that discriminators using spectral normalization can significantly improve inception scores and better stabilize the training process, thereby reducing the likelihood of model collapse.

Orthogonal Regularization (Wang et al., 2015): Although orthogonal regularization was also proposed to address model collapse, Takeru Miyato pointed out that this method might destroy spectral information and give equal importance to all feature dimensions, leading to adverse effects.

Self-Attention Mechanism (Zhang et al., 2019): In 2019, Han Zhang and others proposed that by introducing a self-attention mechanism in the generator (G), the model can better capture longdistance dependencies in images. Experimental results also demonstrated the superior performance of this method in image generation tasks.

To further enhance the image output quality of pix2pix, we can consider combining the above methods and making appropriate adjustments and optimizations. For instance, integrating spectral normalization with the self-attention mechanism can simultaneously improve the stability of the model and the quality of the generated image details.

2.4 Related works "House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation" by (Nauata et al., 2018) presents a novel approach to house layout generation using Generative Adversarial Networks (GAN). The authors propose a system, House-GAN, that generates a diverse set of realistic house layouts under the constraints of a bubble diagram, which is a graph where nodes encode rooms with their types and edges encode their spatial adjacency.

"Apartment Floor Plans Generation via Generative Adversarial Networks" (Hao Zheng et al., 2020)

presents a novel approach to architectural design using machine learning. The authors propose a method to automate the generation of detailed interior design layouts in the early design stage, thereby reducing the workload of architects. The methodology is centred around the use of a Generative Adversarial Network (GAN), a machine learning model designed to learn and generate image data. "Customization and generation of floor plans based on graph transformations" (Wang et al., 2018) introduces an approach for automatic generation of rectangular floor plans based on existing legacy floor plans with the capability of further improvement and customization.

Both articles use GANs to generate images, except that the data input differs "Apartment Floor Plans Generation via Generative Adversarial Networks" (Hao Zheng et al., 2020) used two datasets from previous research in the learning process. The first dataset contains 1279 images of apartment floor plans in Japan, and the second dataset contains 112 images of apartment floor plans in China. The design and drawing styles are different, so two GAN models were trained separately using these two datasets. However, the images in the dataset only contain the design layouts without the boundaries, so the first step was to produce the boundary images based on the plan drawings.

Each image was resized to a bounding area of 500 pixels X 500 pixels and placed in the middle of a canvas of 512 pixels X 512 pixels, leaving a white margin of 6 pixels on each side. Then, a Photoshop script automatically detected the continuous white pixels from the margin to the original image and found the boundary of the plan. By inversely selecting the area and filling black pixels inside the boundary, the boundary image was produced as shown in Figure 1. Thus, the generated boundary image and the resized plan image together became an image pair for the training of GAN.

Figure 1: Testing image pair in each training epoch for the model (Hao, Zheng et al.,2020)

House-GAN uses a relational generative adversarial network (RelGAN) as the core of House-GAN. RelGAN is a type of GAN that is designed to generate structured data, such as graphs. It consists of a generator network and a discriminator network[19]. The generator network takes as input a random noise vector and a graph representing the bubble diagram and generates a set of bounding boxes representing the layout of the rooms. The discriminator network takes as input a set of bounding boxes and a graph and outputs a probability indicating whether the input is a real or fake layout.

House-GAN trains using a dataset of 117,587 house layouts, which they divide into five groups based on the number of rooms as shown in Figure 2. For the generation of layouts in each group, they exclude samples in the same group from the training to ensure that the model cannot simply memorize layouts. After the train was finished, they evaluate House-GAN using three metrics:

realism, diversity, and compatibility. Realism is measured by a user study with graduate students and professional architects, who compare generated layouts with ground truth layouts. Diversity is measured by the Fréchet Inception Distance (FID) score, which quantifies the difference between the distributions of generated layouts and real layouts. Compatibility is measured by the graph edit distance, which quantifies the difference between the adjacency graphs of generated layouts and the input bubble diagrams.

Figure 2: Sample bubble diagrams and house layouts in our dataset (Nauata et al., 2018)

Finally, the author finds that House-GAN outperforms competing methods and baselines in all metrics, except for compatibility against a baseline method with a small margin. They also conduct a qualitative evaluation, which shows that both students and architects rate the layouts generated by House-GAN as the most realistic.

"Customization and generation of floor plans based on graph transformations" (Wang et al., 2018) is quite comprehensive and innovative. The authors have implemented their system, GADG, using JAVA and Eclipse SWT Widget. For an input floor plan with dozens of rooms, GADG generates various alternative layouts within milliseconds. This is demonstrated in a detailed step-by-step case study, where the input IFC file is generated using Autodesk Revit.

Customization and generation of floor plans based on graph transformations (Wang et al., 2018) have enhanced the shape grammar interpreter by allowing users to import shapes and images and to use colours, among other enhanced capabilities. The graphical user interface facilitates users in importing sources and setting rule parameters. In this enhanced interpreter, a dual graph can be generated from any input according to the adjacency relations of rooms and with transformation rules applied to the graph. If the graph is a PTP graph, GADG maps rooms onto the base layout according to the improved rectangular dual-finding algorithm. The interpreter is able to accept any parameters for customization and thus generate layouts to suit a wide range of design requirements.

Both Customization and generation of floor plans based on graph transformations and House-GAN:

Relational Generative Adversarial Networks for Graph-constrained House Layout Generation have employed Generative Adversarial Networks (GAN) using pix2pix for generating building floor plans. As a result, they encounter the typical challenges faced by GAN networks. Frequency features refer to large-scale patterns or changes occurring at a slower rate, capturing overall trends, global structure, or smooth transitions in the input data. These features are common in diverse data types, such as images, audio signals, and time-series data. For instance, in image processing, lowfrequency components encompass the overall brightness, gradient, or large-scale texture of an image.

On the other hand, high-frequency features pertain to fine details or patterns occurring at a faster or higher frequency, capturing localized changes, abrupt shifts, or small-scale features within the input data. Similar to low-frequency features, high-frequency features are found in various data types, including images, audio signals, and time-series data. For example, in image processing, highfrequency components correspond to sharp edges, textures, or fine details like thin lines or dots.

Feature Type

Description

Common in Data Type

Example in image Processing

Low-Frequency Feature

Large-scale patterns or changes occur at a slower rate. Captures overall trends or transitions.

Images

Overall brightness, gradient, and largescale texture.

High-Frequency Feature

Fine details or patterns occur at a faster rate. Captures localized changes or abrupt shifts.

Images

Sharp edges, textures, fine details like thin lines or dots.

Regarding GAN low-frequency preference describes a -model's inclination or bias towards emphasizing high-frequency features while capturing low-frequency features of the input data.

Consequently, the model assigns relatively higher importance or weight to low-frequency components during the learning and prediction processes. These operations reduce the spatial dimensionality of the input data while preserving low-frequency information. Downsampling enables the model to focus on capturing broader context and general trends while ignoring some finer details.

Additionally, Apartment Floor Plans Generation via Generative Adversarial Networks (Zheng et al., 2020) acknowledge that the loss metric alone cannot determine if the model has converged. To assess the model's performance in each training epoch, they feed an input image to the neural network and record the output image. By comparing the generated image with the ground truth, they can determine whether the performance is satisfactory and decide whether to stop the training.

The authors of both studies discuss the limitations of their methods. For instance, Hao Zheng et al.'s method struggles with unrelated rectangular boundary boxes and separate bounding colored areas.

Despite these limitations, they believe that their approach significantly aids architects in the early

design stage by quickly providing previews of architectural plan solutions.

Similarly, Nauata et al.'s project also faces some limitations. It occasionally generates layouts with improper room sizes or shapes for a given room type, misaligned rooms, or inaccessible rooms. To address these issues, they suggest incorporating room size information or door annotations in future work. Additionally, they propose extending House-GAN to generate non-rectangular rooms.

In conclusion, both studies demonstrate the use of GAN and pix2pix for generating building floor plans while acknowledging the challenges and limitations inherent in their approaches. Their research efforts provide valuable insights into leveraging GAN in architectural design and lay the groundwork for further advancements in this domain.

2.5 Systematic Layout Planning In the medical field, numerous layout design examples can be observed, considering the flow and interaction between hospitals, operating rooms, and general intensive care units. Lin introduced a method called Systematic Layout Planning (SLP) to address these challenges (Lin et al., 2015) as shown in Figure 3. System Configuration Design is a time-tested process for planning the configuration of living spaces and has been recognized as a robust method for guiding layout design (Yang, Su, and Hsu 2000). SCD consists of three core steps: creating a relationship diagram that showcases the interconnections and priorities between spaces; formulating a space interaction diagram; and conducting evaluation and feedback (Lin et al., 2015). Qing-Lin and his team also detailed a surgical room configuration design process, which is divided into two key parts as shown in Figure 5: firstly, the equipment configuration phase, where key connections within the operating room are considered and a relationship diagram is created based on them. Following this is the configuration optimization phase, where constraint logic is employed to devise the best plan based on given constraints.

Figure 3: OT facility layout planning procedure (Lin et al. 2015)

2.6 Contribution In this dissertation, the author presents a comprehensive exploration into the enhancement of building plan generation through advanced neural network architectures. Leveraging the foundational framework of Generative Adversarial Networks (GAN), the pix2pix network model

structure is meticulously refined. The innovative approach introduces a self-attention mechanism within the generator and integrates spectral normalisation within the discriminator. This methodology not only ensures higher quality outputs but also provides a mechanism for exerting control over the generated building plans through specific parameters. Moreover, the research pioneers the expansion of GAN applications within the architectural domain. The study delves into the generation of intricate floor plans for both multi-unit residences and multi-storey structures.

Additionally, new territory is charted by examining the synergistic potential of combining GAN with state-of-the-art large language models, thereby setting a robust foundation and direction for imminent architectural research.

3. Methodology and Workflow This chapter has two sub-sections. The initial segment delves into the intricate process of assembling the dataset and meticulously outlines both the structural framework and the training techniques employed in the generative network model. The subsection (3.2), explores the application of this generative model through the lens of a designer. It not only elucidates how a designer can harness the model but also provides a comprehensive overview of the entire workflow involved in utilizing generative networks.

By bifurcating the chapter into dataset assembly and its practical application, we ensure a holistic understanding of both the theoretical and practical aspects of the generative network model. This dual approach not only provides clarity but also establishes the relevance and applicability of the model in real-world scenarios.

3.1 The production of datasets and the web framework In this section, we first delve into the process of dataset selection. We provide a detailed explanation of constructing a custom dataset for training. The potential challenges and issues that might be encountered during this process are also thoroughly discussed. In section (3.1.4) , we shift our focus to the selection of the algorithmic framework. Through an in-depth introduction and analysis, we compare the pros and cons of various algorithmic frameworks, thereby clarifying for the reader why the chosen framework is the optimal choice for this research project. Finally, we present the data records obtained during the training process of this network framework, laying the groundwork for subsequent analysis and discussion.

3.1.1 Controllable Parameters The significance of controllable parameters in architectural design cannot be overstated. These parameters, while technical in nature, have profound implications for the aesthetic and functional outcomes of a design. For instance, the building boundaries, while primarily serving a functional purpose, also influence the visual appeal and integration of the structure within its environment.

Similarly, the positioning of façade openings can dramatically alter the energy consumption of a building, impacting its sustainability and environmental footprint.

The project's controllable parameters are segmented into four critical dimensions, each playing a vital role in the architectural design: 



Building Boundaries: These parameters articulate the overall geometry and footprint of the structure. The boundaries must be carefully considered to align with zoning regulations, aesthetic considerations, and functional requirements, forming the foundational architectural silhouette.

Façade Openings: These elements are pivotal in regulating natural lighting and ventilation.

The positioning and size of windows, doors, and other openings must be optimized to





contribute to the building's energy efficiency, comfort, and visual appeal.

Internal Wall Locations: These specifications facilitate functional zoning and optimal space utilization. The thoughtful placement of internal walls ensures a harmonious and practical layout, allowing for efficient movement and usage within the building.

Specific Functional Areas: This encompasses the strategic alignment of essential zones such as bedrooms, bathrooms, and staircases. The coordination of these areas is a crucial aspect for the coherent design of internal plumbing systems, electrical wiring, and overall flow within the building.

The intricate nature of these parameters necessitated a comprehensive search for suitable existing datasets. However, the specialized requirements rendered existing datasets inadequate, leading to the need for a custom solution.

3.1.2 Dataset Creation The creation of a bespoke dataset was a multifaceted process, involving several critical stages and Figure 4 illustrates a portion of the dataset.

1.

2.

3.

Sourcing Building Plans: Plans were carefully selected from reputable architectural platforms such as ArchDaily and HousePlan. The chosen plans needed to represent a diverse array of architectural styles and configurations, ensuring a quality-driven collection that would be applicable to various design scenarios.

Categorization of House Types: The plans were meticulously categorized by house type, such as single-family homes, apartments, or townhouses. This categorization enhanced the dataset's relevance and applicability, allowing for more targeted analysis and training based on specific housing needs.

Data Cleaning and Pre-processing: A rigorous quality control process was implemented to rectify any inconsistencies or errors within the downloaded plans. This stage involved the removal of irrational designs, correction of wrong labeling, and standardization of formats, ensuring the dataset's integrity and usability.

Figure 4: catalogue image of the dataset created

The creation of this custom dataset not only filled a gap in the available resources but also provided a tailored solution that could significantly enhance the project's outcomes. By aligning the dataset with the specific controllable parameters, the project is now positioned to explore innovative design solutions, conduct more nuanced analysis, and potentially revolutionize the way architectural design is approached.

3.1.3 Data Processing The floor plans sourced from platforms such as ArchDaily and HousePlan are not inherently compatible with direct application in machine learning algorithms. To address this limitation, we have implemented a series of modifications to the floor plans. These alterations involve the utilization of distinct color schemes to symbolize various functional partitions within the layout.

The corresponding color swatches, representing each unique functional area, are delineated in the Figure 5.

Figure 5: color correspondence to different function areas

To achieve the desired outcomes in the three-generation modes, it is imperative to produce three distinct data types: Data Representing Building Boundaries and Façade Entrances: This dataset employs a black fill to delineate the building's footprint. Additionally, white lines are utilized to indicate the positions of the building's internal openings. As shown in Figure 6.

Figure 6: Testing Image Pair with boundary and elevation entrance. Left existing floor plan; Middle boundary plan; Right function area plan.

Data Indicating the Location of Interior Walls: Building upon the foundational data of building boundaries and façade openings, orange lines are introduced to specify the desired placement of the building's interior walls as determined by the user.

As shown in Figure 7.

Figure 7: Testing Image Pair with boundary and internal wall. Left existing floor plan; Middle boundary plan; Right function area plan.

Data Specifying the Position of Functional Zones: This dataset is constructed upon the building boundaries and internal openings. It incorporates colour-coded blocks that correspond to distinct functional zones. The primary objective of this data type is to facilitate the generation of floor plans, with an emphasis on the predetermined positioning of specific functional areas. As shown in Figure 8.

Figure 8: Testing Image Pair with specifying the position of functional zones. Left existing floor plan; Middle boundary plan; Right function area plan.

Multi-unit Building Plan: This dataset is derived from the methodologies used in the first three methods. However, while the initial three focus on single-unit building plans, this dataset encompasses 20 group of multi-unit data, as illustrated in Figure 9.

Figure 9: Multi-unit building function area plan.

Multi-storey Building Plan: Similarly, this dataset is founded on the first three methods. In this category, the Ground Floor Plan is presented on the left, while the First Floor Plan is on the right. This dataset also consists of 20 groups, depicted in Figure 10.

Figure 10: Multi-story building function area plan.

In this project, the dataset comprises 640 pairs. Of these, 200 pairs are designated for building boundaries and façade elements, another 200 pairs are for known locations of internal building walls, and the remaining 200 pairs pinpoint the locations of specific functional partitions, 20 multi-unit building plans and 20 multi-storey building plans.

3.1.4 Network Structure In the thesis, the Generative Adversarial Networks (GANs) model, which amalgamates both convolutional and anti-convolutional kernels, has been employed. This model, as delineated by Goodfellow et al. (2014), is lauded for its precise correspondence between the input and the resultant planar graph, a feature indispensable in intricate image processing tasks where accuracy is paramount. The working principle is shown in Figure 11.

Figure 11: Diagram of GAN (Generative Adversarial Networks – Hot Topic in Machine Learning n.d.)

For the practical implementation of our research objectives, we have opted for the open-source framework, pix2pix, pioneered by (Isola et al., 2017). This selection was predicated on the framework's demonstrated efficacy in image-to-image translation endeavours and its inherent adaptability to a myriad of datasets.

The foundational architecture of the pix2pix framework (Isola et al, 2017) is characterized by its distinct components. The generator (G) is intricately designed, encompassing convolutional, residual, and inverse convolutional layers. These layers collaboratively function to metamorphose the input image, ensuring the preservation of salient features whilst introducing requisite alterations, and continually generating images that resemble the original functional zoning map of the building.

Concurrently, the discriminator (D) serves as an arbiter of quality, juxtaposing the synthesized image against its original counterpart and furnishing iterative feedback to the generator for distinguishing between the (G)-generated image as well as the original functional zoning map. This interdependent relationship engenders a cycle of refinement, with the generator perpetually enhancing its outputs and the discriminator honing its evaluative acumen.

3.1.5 Network Training During the intricate training phase was conducted for 300 epochs and each training session took 403 minutes, Figure 12 meticulously captures the fluctuating loss values for both the generator and the discriminator. These values are crucial in understanding the model's learning trajectory.

Each type of loss is visually represented by a distinct color for clarity: the blue line is reserved for the generator's loss, the orange line specifically for the overall loss of the generative adversarial network, the green line for the discriminator's evaluation of fake images, and the red line for its assessment of real images. One of the standout observations from this data is the intriguing inverse relationship between the generator and discriminator losses. In a typical scenario, when the generator becomes more proficient, leading to a decrease in its loss, the discriminator finds it challenging to differentiate, causing its loss to increase. This tug-of-war dynamic is a hallmark of the adversarial competition inherent in the design of GANs. The pattern observed in the data not only provides insights into the model's performance but also serves as a testament to the efficacy of the training regiment.

Figure 12: Training data

Diving deeper into the training mechanics, the model's weights undergo initialization using a Gaussian distribution. This distribution is characterized by a mean of 0 and a standard deviation of 0.02. Such a methodological approach to initialization is not arbitrary; it is rooted in the objective of ensuring stability in the learning process and hastening the convergence rate. To further bolster the model's robustness and versatility, the training data undergoes enhancements. Techniques such as random jittering and mirroring are employed, introducing variability and unpredictability. This not only challenges the model during training but also equips it with the capability to generalize effectively to data it hasn't encountered before.

Figure 13 Images generated during training. The initial images, reminiscent of a learner's first steps, display evident challenges like blurring and vague boundaries. These imperfections, however, don't persist. As the training progresses, a transformation is evident. The images evolve, showcasing crisper quality and more defined functional areas. The colors, once overlapping, now respect their boundaries, leading to clearer and more distinct images. Recognizing the maturity and capability of the model, training was deliberately halted at the 300th iteration. Post this, the model was put to the test, with its skills being utilized to interpret and analyze floor plans.

Figure 13: Images generated during training.

3.2 Workflow In this part through an exhaustive analysis of the operational patterns of designers, we introduce a workflow that more closely aligns with the routine practices and proclivities of designers as shown in Figure 14. Recognizing the challenges designers may encounter in practical implementations, we have incorporated the GANs technology, aiming to guide and streamline their work. Once the design outcomes are generated, it is imperative for designers to conduct thorough evaluations, rectify elements that deviate from architectural standards or exhibit logical inconsistencies, and further refine the functional zoning.

The workflow, as outlined, is not just a procedural roadmap but also a strategic approach to harnessing the full potential of the generative model. By aligning the workflow with designers' operational patterns, we aim to bridge the gap between theoretical modelling and practical design application. This alignment ensures that the model's outputs are not just technically sound but also intuitively resonant with designers' creative processes.

Figure 14: Diagram of Workflow.

3.2.1 Defining Architectural Boundaries Boundary Determination: First and foremost, it's essential to analyze the shape and size of the plot to establish potential architectural boundaries. This process may involve considerations related to the terrain, nearby structures, regulatory constraints, and other relevant factors.

3.2.2 Image Input Creation Filling Process of Architectural Areas: To delineate the primary structure of the architecture, we employ a black fill within the building's boundaries and utilize white lines to annotate the positions of internal openings. This approach facilitates the precise identification of pivotal architectural features, such as main entrances and ventilation ports.

Recognition and Annotation of Inner Wall Positions: To gain a deeper understanding of the building's internal structure, we employ orange lines to annotate potential inner wall locations.

This method of annotation aids in a lucid comprehension of the building's internal structure and spatial layout.

Annotation of Functional Areas: Given the diversity and functional requirements of architecture, we use varied colors to annotate specific functional zones. This strategy ensures a rational layout of these areas and fosters effective synergy among them.

3.2.3 Image Pairing Techniques Arbitrary Image Generation: Within the pix2pix image processing framework, a core requirement is that data must be presented in pairs. For this purpose, researchers need to create an arbitrary image that matches the dimensions of the architectural boundary diagram. Such images can be designed based on various visual elements, such as texture features, color gradient techniques, etc.

Image Synthesis Strategy: To meet the subsequent image processing and analysis needs, researchers should precisely merge the architectural boundary diagram with the aforementioned arbitrary image, ensuring that they form a complete paired dataset as shown in Figure 15 and Figure 16.

Figure 15: Testing data with boundary and elevation entrance. Left: existing building plan; Right: boundary plan

input.

Figure 16: Testing data with boundary and elevation entrance. Left: existing building plan; Right: boundary plan input.

3.2.4 Iterative Process of Architectural Image Generation The iterative process underscores the dynamic nature of design. With each iteration, the model refines its outputs, drawing closer to a design that is both innovative and functional. This iterative approach mirrors the traditional design process, where architects and designers continually refine their drafts in pursuit of perfection. However, by leveraging the power of the generative model, this research accelerates this iterative process, achieving in minutes what might traditionally take days or even weeks.

Iterative Generation Strategy: Through continuous iterations of architectural image generation, researchers can obtain a broad collection of design solutions, providing a richer selection space for subsequent screening.

Quality Selection Mechanism: Among the generated image set, a meticulous quality assessment is required to ensure that the selected output images meet the expected standards in detail processing, proportional relationships, and aesthetic principles.

3.2.5 Design Evaluation and Continuous Optimization Professional Assessment: Designers must conduct a comprehensive professional evaluation of the output design results, ensuring they comply with industry standards, functional requirements, and aesthetic criteria, as well as their feasibility in practical applications.

Continuous Optimization Strategy: For any illogical or imperfect design elements in the output results, designers have the responsibility to delve into modifications and optimizations. This ensures that the final design solution is not only feasible but also achieves an exceptional design level. Moreover, designers are encouraged to incorporate personal innovative elements, making the design more distinctive and creative.

4. Result This chapter is divided into two main sections. The section (4.2) compares the pix2pix model without the introduction of the self-attention system and spectral normalization with the model that incorporates these techniques in terms of architectural floor plan generation. Through experiments, we verify whether these techniques can optimize the generation of architectural floor plans and explore the areas they have improved. The section (4.4) delves into the generation of multi-unit architectural floor plans, examining whether GANs models can generate multi-unit or multi-layer architectural floor plans.

4.1 Compare two generations of different GAN. Before testing the results, both the unmodified pix2pix and the pix2pix with the introduced selfattention system and spectral normalization were trained for 300 epochs. We also ensured that the data used for testing was not involved in previous network training to guarantee the accuracy of the tests.

4.2 For tests on known architectural boundaries and internal openings Figures 17 and Figure 18 display the test results for the same set of floor plan data. Figure 7 shows the generation results without the introduction of the self-attention system and spectral normalization. It can be observed that without these techniques, the quality of the generated image is lower, especially in terms of data capture at architectural boundaries. Although there is some color mixing in the internal functional area division, the overall generation quality is still acceptable.

Figure 8 presents the results after introducing the self-attention system and spectral normalization.

Although the size of the generated balcony area does not fully match the original, the division of functional areas within the architectural boundary is satisfactory. This is of great significance for architects during the initial phase of functional area division.

Figure 17: Generation result without optimization – boundary and elevation entrance

Figure 18: Generation with optimization - boundary and elevation entrance

4.3 Generating images with fixed internal walls Figures 19 and Figure 20 respectively show the generation results without and with the introduction of the self-attention system and spectral normalization. The quality of the architectural floor plans generated by both methods is quite high. To validate this, we tested 6 different sets of architectural floor plans, and the results were consistent with this experiment.

` Figure 19: Generation without optimization - boundary and internal wall

Figure 20: Generation with optimization - boundary and internal wall

4.4 Generating architectural floor plans after fixing specific functional areas The results in Figures 21 and Figure 22 are similar to the above, regardless of whether the selfattention system and spectral normalization were introduced, the quality of the generated architectural floor plans remains high.

Figure 21: Generation without optimization - boundary and specifying the position of functional zones.

Figure 22: Generation with optimization - boundary and specifying the position of functional zones.

4.2 Multi-unit Building Floor Plans: The single-unit building floor plan generation was assessed in the previous section. While the results were discernible to architects, some areas exhibited color blurring. For context, Hao Zheng and Nelson Nauata have previously attempted to output single-unit building floor plans using different methodologies.

To determine if GANs can handle more intricate designs like multi-unit or multi-storey buildings, further tests were conducted: Figure 12 displays the GANs' output based on Figure 23. While the building boundary is accurately captured, some internal functional areas exhibit color mixing.

Figure 23: Generation result for Multi-unit building function area plan.

The generation test for such buildings is categorized into two modes: one without constraints and the other with fixed locations for stairs and bathrooms.

Figure 24 depicts the generated plan with only building boundaries and façade openings. Without marked staircase positions in the input, the output lacks staircases, making it more akin to a multiunit plan than a multi-storey one.

Figure 24: Generation result for Multi-story building with boundary and elevation entrance.

Figure 25 presents the results with marked staircase and bathroom locations. Unfortunately, the output is subpar and not usable for architectural design (Note: potential resaon for sub par outputs).

Figure 25: Generation result for Multi-story building with boundary and toilet/stair position of functional zones.

5. Discussion 5.1 Analysis and Interpretation of Results The project aimed to compare the performance of the pix2pix model before and after the introduction of the self-attention system and spectral normalization. Our experiments demonstrated that incorporating the self-attention system into the generator (G) significantly improved the model's generation quality, as evident in Figures 7 and 8.

On the other hand, introducing spectral normalization to the discriminator (D) didn't yield a significant enhancement in quality. Interestingly, even though the quality of the generated images was not high, the model did not experience any crashes during the 20 iterations of generation after the introduction of spectral normalization. This suggests that while spectral normalization might not directly improve image quality, it could provide stability to the discriminator (D) of the pix2pix model.

Furthermore, the pix2pix model displayed a higher efficacy in generating single-unit building plans compared to multi-unit ones. Several factors could account for this: 

Dataset: The training data, sourced from ArchDaily and HousePlan, predominantly consists of single-unit and single-story building plans. This imbalance, with single-unit plans vastly outnumbering multi-unit ones, might cause the pix2pix model to overfit the training data.

Consequently, while the model excels with the training data, it might underperform with unseen test data, especially multi-unit plans.



Model Configuration: The model's loss function settings might not effectively capture the differences between the multi-unit model predictions and the actual values. This could lead to biased results when minimizing this difference, resulting in subpar generation of multi-unit building floor plans.

In our experiments focused on generating multi-storey building floor plans, the GANs network struggled to produce multi-storey plans based solely on building boundaries and other information, especially when staircase locations weren't labelled. Even with labelled staircase locations, the generated quality was unsatisfactory and unlikely to meet designer standards. A potential reason for this could be the limited number of multi-storey floor plans used in training. While single-unit training utilized 450 sets, multi-storey floor plan training had only 20. We anticipate that increasing the dataset to around 200 sheets might significantly enhance the generation quality.

5.2 Significance of the Findings This study underscores the transformative potential of GANs in producing high-caliber architectural floor plans. By automating this initial phase, architects can channel their energies into refining and personalizing designs, rather than building from the ground up. This streamlines the design journey,

offering a swift visualization of multiple layouts. Furthermore, by embedding controllable parameters within the GAN model, architects gain a tool that balances functionality with aesthetics, ensuring the output is both practical and visually appealing.

Our in-depth examination of the training mechanisms highlights the meticulous steps undertaken to optimize the GAN model. The model's progression, from initial hurdles to producing lucid and unequivocal floor plans, attests to its viability in real-world scenarios.

Merging GANs with conventional computer-aided design (CAD) tools could be a game-changer for architectural design. This study sets the stage for a paradigm where AI-driven tools like GANs collaborate with CAD software, infusing dynamism, efficiency, and innovation into the design process.

In summary, this research's revelations could redefine the architectural design domain. By leveraging GANs, we introduce a pioneering method for floor plan generation, poised to revolutionize design toolkits and methodologies.

5.3 Limitations of the Research While the research has paved a promising path using GANs for architectural design, it's crucial to recognize the challenges faced: Single vs. Multi-Unit Plans: The GAN model, although advanced, showed a marked proficiency in single-unit designs over multi-unit ones. This difference could be attributed to potential data imbalances during the training phase, suggesting a risk of model overfitting.

Data Availability for Multi-Storey Designs: A significant challenge arose from the limited data on multi-storey buildings. This scarcity, especially noticeable when staircases weren't explicitly marked, inevitably impacted the model's ability to generate intricate designs.

Pursuit of Image Excellence: The introduction of a self-attention mechanism undeniably improved image quality. However, the journey towards achieving impeccable image clarity still has room for progress.

Loss Function Intricacies: The model's loss function sometimes struggled to accurately gauge the differences between predicted and actual values, especially for multi-cell models. This discrepancy highlights the need for further investigation and refinement.

Pix2Pix Input Constraints: Since the pix2pix input images must be in pairs, even though pairs can be arbitrary images of the same size, this requirement introduces cumbersome steps in producing the generated images, adding complexity to the process.

6. Conclusion 6.1 Initial Aim In the rapidly evolving landscape of Advanced Intelligent Generative Computing (AIGC) and Artificial Intelligence, this research was conceived with a clear and ambitious vision. The overarching aim was to instigate a transformative shift in the architectural design process, a domain that has long been characterized by iterative and resource-intensive cycles. Traditional design methodologies, while integral to the evolution of architectural concepts, often demand substantial manpower, intellectual investment, and time inefficiencies. Recognizing these challenges, the research sought to harness the power of innovative machine learning techniques. Specifically, the focus was on the development of a tool that would equip architects with efficient and creative design solutions right from the project's inception. Central to this vision was the Generative Adversarial Network (GAN), a groundbreaking technology known for its ability to learn from and generate realworld images, encompassing architectural floor plans.

6.2 Key Points and Results The journey of this research unveiled the untapped potential of GANs in revolutionizing the architectural design domain. By leveraging GANs, a novel method for floor plan generation was introduced, holding the promise to redefine design toolkits. The GAN model, through its intricate algorithms and learning mechanisms, showcased the capability to transform rudimentary input images, which depicted design boundaries and façade openings, into detailed, functional, and aesthetically pleasing interior designs. The results, as evidenced by the model's outputs, were indicative of a future where computers could autonomously generate designs that were both functional and met design, structural, environmental and other architectural constraints.

6.3 Future Work Some of the potetial future work are int the domains of: 

Dataset Refinement: The predominant use of residential floor plans from sources like ArchDaily and HousePlan limits the model's versatility. Future endeavours could incorporate diverse building types, such as museums or commercial centres, to enrich the dataset and better aid designers.



Enhancing Multi-Unit Floor Plan Generation: The intricacies of multi-unit designs, especially concerning internal functional area colour blending, indicate a need for GAN model refinement.



Addressing Layout Issues: Subsequent research could tackle specific layout challenges, like misproportioned rooms or misaligned spaces. Infusing data like room dimensions or door annotations might prove beneficial.



Venturing Beyond Rectangular Rooms: To foster creativity, future efforts might delve into generating non-rectangular rooms.



Model Improvement: Addressing prevalent GAN challenges, such as model instability or a bias towards low frequencies, is crucial. Enhancing details, especially in color transitions, is vital for a more realistic output.



Integration with Large Language Models: Given the buzz around Large Language Modelling, a promising avenue might be using linguistic prompts to generate floor plans. Future endeavours could explore synergies between NVIDIA's Megatron Large Language Modelling framework and GANs.

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