This repository contains the full research and survey workflow, source code, experiments, datasets, and results related to our Project.
Our Paper: "A State-of-Art Survey on Generative AI Techniques for Floor Planning"
The goal of this paper is to explore how modern generative AI models, particularly text-to-image architectures like Stable Diffusion, can be applied to synthesize architectural floor plans from textual descriptions.
⚠️ Note: The paper itself is not about the specific models we created in this repository. Instead, it is a survey-based study that reviews how architectural floor plans are being generated using generative AI techniques in the current research and industry landscape. The included models and prototypes were developed as part of our own exploratory implementations inspired by the techniques surveyed.
📢 Our paper was accepted to the workshop GenAICHI 2025 at the prestigious CHI 2025 conference.
- 🗓️ Workshop Date: April 27, 2025
- 🧩 Workshop: GenAICHI 2025 – Generative AI and Human-Computer Interaction
- 🧠 Conference: CHI 2025 – ACM Conference on Human Factors in Computing Systems
- 📄 Read the Paper: View PDF
├── .github/
│ ├── ISSUE_TEMPLATE/
│ │ ├── bug_report.md
│ │ └── feature_request.md
│ └── README.md
│
├── Code/
│ ├── Data Augmentation Code/
│ │ ├── Image_Augmentation.ipynb
│ │ └── README.md
│ ├── Main Source Code/
│ │ ├── Floor Planning GEN AI.ipynb
│ │ ├── floor_planning_gen_ai.py
│ │ ├── requirements.txt
│ │ └── README.md
│ └── README.md
│
├── Datasets/
│ ├── Augmented Dataset/
│ │ ├── bright_augmentation.jpg
│ │ ├── colored_augmentation.jpg
│ │ ├── dark_augmentation.jpg
│ │ ├── flipped_augmentation.jpg
│ │ ├── grayed_augmentation.jpg
│ │ ├── noisy_augmentation.jpg
│ │ ├── rotated_augmentation.jpg
│ │ └── README.md
│ ├── Original Dataset/
│ │ └── README.md
│ └── README.md
│
├── Papers/
│ ├── Graphs and Charts/
│ │ ├── flowchart.png
│ │ ├── graph.png
│ │ ├── pie chart.png
│ │ └── titled graph.png
│ ├── Tables/
│ │ ├── Accuracy Table of papers.csv
│ │ ├── Datasets of papers.csv
│ │ ├── Final Table of Research Papers.csv
│ │ ├── Links of Datasets of papers.csv
│ │ ├── Search Strings.csv
│ │ └── Technologies table.csv
│ └── README.md
│
├── Prototypes/
│ ├── 01_First Prototype/
│ │ ├── First_Prototype.ipynb
│ │ └── README.md
│ ├── 02_Second Prototype/
│ │ ├── Second_Prototype.py
│ │ └── README.md
│ ├── 03_Third Prototype/
│ │ ├── Third_Prototype.ipynb
│ │ └── README.md
│ └── README.md
│
├── Results/
│ ├── IMAGE 1.png
│ ├── IMAGE 2.png
│ ├── IMAGE 3.png
│ ├── IMAGE 4.png
│ └── README.md
└── LICENSE
- Code/: Contains all implementation files, including model training, inference, and data augmentation code.
- Datasets/: Organized into the original dataset reference and the augmented dataset created during the project.
- Papers/: Includes visualizations, graphs, and raw table data used in our paper.
- Prototypes/: Experimental models built using Stable Diffusion to generate floor plans from text.
- Results/: A small set of output images generated from the trained models and prototypes.
-
Clone this repo:
git clone https://github.com/Jyotibrat/A-State-of-Art-Survey-on-Generative-AI-Techniques-for-Floor-Planning.git
-
Run the Colab notebooks or Python scripts from the respective prototype folders.
If you use this repository or refer to our work, please cite:
JAIN, ANKUR, BINDUPAUTRA JYOTIBRAT, ARUNIM GOGOI, RANA TALUKDAR, AVINASH KUSHWAHA, and NAVJOT SINGH GILL. "A State-of-Art Survey on Generative AI Techniques for Floor Planning." (2025).
@article{jain2025state,
title={A State-of-Art Survey on Generative AI Techniques for Floor Planning},
author={JAIN, ANKUR and JYOTIBRAT, BINDUPAUTRA and GOGOI, ARUNIM and TALUKDAR, RANA and KUSHWAHA, AVINASH and GILL, NAVJOT SINGH},
year={2025}
}🔖 License
This repository is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
Full license details: https://creativecommons.org/licenses/by/4.0/
Special thanks to:
- The authors of the ROBIN dataset.
- Developers and maintainers of Stable Diffusion, Hugging Face, PyTorch, and the open-source ML community