FITVERSE: AN AI-POWERED FASHION INTELLIGENCE PLATFORM FOR REAL-TIME BODY MEASUREMENT EXTRACTION AND PERSONALIZED FASHION RECOMMENDATION USING MEDIAPIPE AND COMPUTER VISION

Abstract

Ecommerce has also revolutionized the fashion retail market and created a convenient shopping experience for consumers to buy fashion products online. Fitting problems, return of products, and wrong size fit before purchasing, however, are frequent issues such as the ability to touch, feel and try-on the garment before taking a chance is denied. Unfortunately, the trend of fashion recommendations with date for users is primarily based on the user's preferences, a mapping with fixed dimensions, purchasing history, or any other kind of information that does not yield a detailed and dynamic relationship between fashion and body attributes that provides a better understanding of user satisfaction. This paper introduces a new Fashion Intelligence System, called FitVerse, that deals with these challenges. The proposed system is based on the real-time body landmark detection system provided by Mediapipe, which can be used to detect and extract the body landmarks and a multi-image analysis based on the webcam scan for obtaining the body measurements. It provides you with its own "keys" for the body measurements – chest, waist, hips, shoulders, thighs, inseam and height – and correlates them to sizes offered by different brands precise to clothing size. Besides, the Fashion Intelligence Engine created by FitVerse could carry out body-shapes classification, suggestions of fit type and color analysis of skin tone, which enhances the level of personalization. The experimental evaluation shows that the proposed methods can be applied in real time with good performance in measuring body dimensions, classification, and accuracy of the recommendations.

Keywords: Artificial Intelligence, Fashion Recommendation System, MediaPipe, OpenCV, Computer Vision, Pose Estimation, Personalized Outfit Suggestions