TECHNOLOGIES FOR DEVELOPING A “VIRTUAL FITTING ROOM”

Abstract

Artificial Intelligence (AI) has revolutionized various sectors, including e-commerce, by enhancing customer experiences and optimizing business operations. This paper delves into the implementation of a virtual fitting room powered by AI, focusing on its ability to address critical challenges such as high return rates, customer dissatisfaction, and inefficiencies in the online shopping process. The study highlights the integration of advanced generative models, such as Generative Adversarial Networks (GANs) and Diffusion Models, which enable realistic and precise virtual try-ons. Additionally, segmentation technologies like Mask R-CNN are employed to accurately analyze user-provided images and extract key visual features for further processing.

The research explores the role of AI-driven recommendation systems, which analyze user preferences and historical data to deliver personalized product suggestions, thereby increasing customer loyalty and satisfaction. These technologies collectively transform the shopping experience, making it more interactive and tailored to individual needs. The findings demonstrate how virtual fitting rooms can significantly reduce return rates by addressing issues related to incorrect sizing, style mismatches, and user hesitation. Moreover, the application of AR/VR technologies is discussed as a complementary tool, enhancing the visual and functional aspects of the fitting room experience.

This paper also examines the broader implications of AI in e-commerce, including operational efficiency, inventory management, and logistics optimization. By leveraging AI, businesses can gain insights into consumer behavior, streamline their supply chains, and minimize environmental impacts associated with excessive returns. Furthermore, the study investigates the adaptability of these systems across various industries, such as furniture, cosmetics, and accessories, showcasing the versatility of AI technologies.

The architecture of the virtual fitting room is thoroughly analyzed in this research, encompassing various components and their interactions. The system is designed to integrate key modules such as: user Interface (UI): A web and mobile application interface for seamless interaction with end-users; client API and API Gateway: Serving as intermediaries for data exchange and managing requests between front-end and back-end systems; image Preprocessing Module: Responsible for normalizing and preparing user-provided images for segmentation; segmentation Service (Mask R-CNN): Processes preprocessed images to create accurate masks for generative modeling; generative Model Service (GANs/Diffusion Models): Produces realistic textures and overlays to simulate virtual try-ons; recommendation Engine: Delivers personalized product suggestions based on user data and preferences; e-commerce API Integration: Ensures the availability of product metadata and catalog information for a cohesive shopping experience.

The study underscores the scientific and practical contributions of virtual fitting rooms. Realistic Visualization: Combining state-of-the-art generative models with segmentation technologies for accurate virtual try-ons. Personalization: Leveraging AI-driven recommendation engines to enhance user experience. Operational Impact: Reducing return rates and improving inventory management through predictive analytics. Cross-industry Adaptability: Highlighting the potential applications of virtual fitting room technologies in other domains, such as furniture and cosmetics. This research provides an in-depth understanding of how AI can revolutionize online retail experiences and sets the stage for future innovations in AI-powered e-commerce solutions.