Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal for Research in Applied Science and Engineering Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
