FITSTYLE: An Application Revolutionizing Online Shopping by Enhancing the Virtual Try-On Experience

Abstract

In the last decade, the fashion industry has been significantly influenced by the rise of e-commerce and mobile commerce. 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 negatively impact customer satisfaction and online sales productivity, highlighting the need for advanced virtual shopping solutions. To address this problem, FITSTYLE proposes a sophisticated virtual try-on tool based on Generative Adversarial Networks (GANs) to simulate how clothes fit a consumer’s body. The FITSTYLE system tackles challenges in online clothing shopping by utilizing advanced technologies such as ResNet101 for image preprocessing, OpenPose for pose estimation, image segmentation to isolate users from backgrounds, and garment deformation algorithms for accurate fitting. Designed for ease of use and realistic visualization, it enhances customer satisfaction and reduces return rates. The research employed a mixed quantitative and qualitative methodology, collecting data from online shoppers to understand user needs and preferences. Based on these insights, the system was implemented using the most suitable technologies, with initial results showing improved customer decision-making and engagement. The system enhances satisfaction, reduces return rates, and boosts productivity in online sales, while also paving the way for future advancements in virtual try-on technologies and the broader fashion e-commerce landscape.