FITSTYLE: A GAN-Based Application Revolutionizing Online Shopping by Enhancing the Virtual Try-On Experience

In the last decade, the fashion industry has been significantly influenced by the increasing e-commerce and mobile commerce trends. As online consumers, we often face the challenge of selecting the right clothing size without the ability to physically try on garments, leading to frustration, uncertainty, and high return rates. These issues impact on customer satisfaction and online sales productivity, highlighting the need for advanced virtual shopping solutions. To overcome this problem, FITSTYLE proposes a sophisticated virtual try-on tool based on Generative Adversarial Networks (GANs) to simulate the fit of clothes on the consumer's figure. The FITSTYLE system addresses challenges in online clothing shopping by using advanced technologies like ResNet101 for image preprocessing, OpenPose for pose estimation, image segmentation to isolate users from backgrounds, and clothes deformation algorithms for accurate garment fitting. Designed for ease of use and realistic visualizations, it enhances customer satisfaction and reduces return rates. The research employed a combined quantitative and qualitative methodology, gathering data from online shoppers to identify user needs and preferences. Based on these insights, the system is implemented using the most suitable technologies, with initial results showing improved customer decision-making and engagement, paving the way for advancements in virtual try-on technologies. The system enhances customer satisfaction, reduces return rates, and improves productivity in online sales. Furthermore, it opens new opportunities for advancements in virtual try-on technologies, paving the way for future developments in fashion e-commerce.