Year: 2024
Type: article
Abstract: : The integration of Generative AI in interior design has transformed traditional methods,allowing designers to explore new concepts with impressive efficiency. This paper presents a comparative study of leading generative models—StyleGAN, Variational Autoencoders (VAEs), Pix2Pix, and Reinforcement Learning (RL)—evaluating their effectiveness in turning sketches into photorealistic renderings, generating diverse room layouts, and optimizing spaces. By analyzing the results of these models, we show their ability to create unique design solutions that meet functional requirements while enhancing aesthetic appeal. The study highlights substantial enhancements in design precision, emphasizing the potential of generative AI models to elevate the design process and create more tailored interior solutions. This survey examines the methods and performance of each model and looksat future possibilities for using Generative AI to advance the field of interior design.
Authors: Revati Anil Patil, Prof. Vrushali Wankhede, Bhagyashree Patil
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4403565182