Automation in Interior Space Planning - Utilizing Conditional Generative Adversarial Network Models to Create Furniture Layouts

Oct 29, 2026

Upload by 658056

In interior space planning, furnishing typically requires manual iterative workflows: aligning with design objectives, incorporating expert input, and optimizing performance. This study develops an ML-driven furnishing approach to enhance design processes while preserving creativity and quality. It also introduces evaluation metrics to compare results across models. Using tagged floor plans, a dataset trains and tests three conditional GAN models (pix2pix, BicycleGAN, SPADE) to generate furniture layouts constrained by room boundaries, with additional post-processing improvements.

LanguageEnglish

CategoryTechnology

FormatPDF

Pages20

Reading Time50 min

AccessFree to download

Abstract

Keywords

1. Introduction

buildings Article Automation in Interior Space Planning: Utilizing Conditional Generative Adversarial Network Models to Create Furniture Layouts Hanan Tanasra 1, *, Tamar Rott Shaham 2, Tomer Michaeli 2, Guy Austern 1,† and Shany Barath 1,† Citation: Tanasra, H.; Rott Shaham, T.; Michaeli, T.; Austern, G.; Barath, S. Automation in Interior Space Planning: Utilizing Conditional Generative Adversarial Network Models to Create Furniture Layouts. Buildings 2023, 13, 1793. [https://](https://)[ ](https://)[doi.org/10.3390/buildings13071793](doi.org/10.3390/buildings13071793) Academic Editor: Derek Clements-Croome Received: 29 May 2023 Revised: 26 June 2023 Accepted: 10 July 2023 Published: 14 July 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . 1 Architecture, Technion, Haifa 3200003, Israel; [guyaustern@technion.ac.il](guyaustern@technion.ac.il) (G.A.); [barathshany@technion.ac.il](barathshany@technion.ac.il) (S.B.) 2 Electrical and Computer Engineering, Technion, Haifa 3200003, Israel; [tamarott@mit.edu](tamarott@mit.edu) (T.R.S.); [tomer.m@ee.technion.ac.il](tomer.m@ee.technion.ac.il) (T.M.) * [Correspondence: hanantn@campus.technion.ac.il](Correspondence: hanantn@campus.technion.ac.il)[ ](Correspondence: hanantn@campus.technion.ac.il)† These authors contributed equally to this work. Abstract: In interior space planning, the furnishing stage usually entails manual iterative processes, including meeting design objectives, incorporating professional input, and optimizing design performance. Machine learning has the potential to automate and improve interior design processes while maintaining creativity and quality. The aim of this study was to develop a furnishing method that leverages machine learning as a means for enhancing design processes. A secondary aim was to develop a set of evaluation metrics for assessing the quality of the results generated from such methods, enabling comparisons between the performance of different models. To achieve these aims,ﬂoor plans were tagged and assembled into a comprehensive dataset that was then employed for training and evaluating three conditional generative adversarial network models (pix2pix, BicycleGAN, and SPADE) to generate furniture layouts within given room boundaries. Post-processing methods for improving the generated results were also developed. Finally, evaluation criteria that combine measures of architectural design with standard computer vision parameters were devised. Visual architectural analyses of the results conﬁrm that the generated rooms adhere to accepted architectural standards. The numerical results indicate that BicycleGAN outperformed the two other models. Moreover, the overall results demonstrate a machine-learning workﬂow that can be used to augment existing interior design processes. Keywords: machine learning; interior space planning; CGANs; image-to-image translation 1. Introduction To provide a clear representation of a given design plan, comprehensive drawings of the walls, doors, windows, and furniture are required. Designers tend to manually depict their furnishing work using Computer-Aided Design (CAD) software based on their knowledge and experience and in line with their clients' requests [1] . Optimally integrating furniture items within ﬂoor plans plays a pivotal role in various stages of the design process. Not only do furniture arrangements serve to unveil and communicate the quality of a given space, but they also accentuate the space's functionality and performance. As such, when incorporating furniture items into ﬂoor plans, interior designers meticulously consider a wide array of factors in order t

FAQs

What is the main goal of the study on interior space planning automation?

Develop an ML-based method for automated room furnishing to enhance interior design processes during the workflow.

Which conditional generative adversarial network models are trained for furniture layout generation?

The study trains pix2pix, BicycleGAN, and SPADE to generate furniture layouts from unfurnished plans.

How are the generated furniture layouts evaluated and compared?

The work develops evaluation criteria combining architectural design measures with standard computer vision parameters, enabling model performance comparisons.

Oct 29, 2026

Upload by 658056