Description

This project involves the development of an AI-driven sizing and fit recommendation platform using Augmented Reality (AR), Computer Vision, and Machine Learning to address inaccurate apparel sizing in online fashion commerce. The system is designed to enable users to capture body measurements using a standard mobile device camera, without requiring specialised hardware or body scanning equipment. The core of the solution relies on computer vision pipelines to perform multi-frame body analysis, depth approximation, and posture normalisation from 2D images or short video captures. Advanced algorithms compensate for clothing layers, lighting conditions, and camera angles to generate accurate body dimensions. These measurements are then processed through an AI-based recommendation engine that maps user body profiles to brand-specific size charts and garment fit models. The platform includes mobile applications (iOS and Android) for end users and a web-based SaaS backend for retailers. The backend provides APIs for retailer integration, size-mapping configuration, analytics, and fit accuracy feedback. The system supports secure user authentication, profile management, data privacy controls, and scalable cloud-based processing. Additional components include fit confidence scoring, return-rate analytics, and continuous model improvement using anonymised interaction data. The architecture is designed to be modular, scalable, and compliant with data protection standards, enabling deployment across multiple regions and retail partners. Overall, the solution aims to significantly reduce product returns, improve customer satisfaction, and optimise operational efficiency for fashion retailers by transforming raw visual data into reliable, actionable sizing intelligence.

Goals

Skills Required