Internet fashion stores are still plagued with high rates of returns as a result of faulty sizing, lack of three dimensional images of the garment and two dimensional displays of the items which are not a true reflection of the body proportions. These issues and their operational/environmental effects have been reported previously and in the industry reports [5, 14]. This manuscript reports a personalized avatar-based virtual fitting platform which enables realistic 3D avatars [10], automated GLTF-to-GLB asset conversion based on glTF standard [6], real time browser-based implementation with Three.js, and cloud deployment with the use of Docker and Terraform [7, 8]. This system is made up of a Next.js native, FastAPI and Node.js, and a Python learning microservice (doing preprocessing and geometry registration) [15] and high-level user evaluations indicate an average rendering latency below 3 s, 97-percent preprocessing SPR on a mixed-asset test set, and favorable responses in a small study: The integrated pipeline is designed to cut down the returns through accuracy with regard to size and visualization.