TRY ON
QuickFit Try-On

QuickFit – Virtual Try-On Experience

Cross-platform virtual try-on application using deep learning for human parsing, pose estimation, and segmentation to visualize clothing before purchase.

ReactDjangoYOLOFastSAMDensePoseDCI-VTON

Project Overview

QuickFit is a cross-platform virtual try-on application that allows users to visualize how clothing items will look on them before making a purchase. It combines deep learning-based human parsing, pose estimation, and segmentation to offer an interactive and personalized fashion experience. Designed for both privacy-conscious and style-focused users, QuickFit helps reduce product returns and boosts confidence in online shopping.

How It Works

Photo Upload
User uploads a photo or captures one using camera.
Clothing Upload
Clothing image is uploaded from gallery or camera.
YOLO Detection
YOLO detects and crops the clothing item.
FastSAM Segmentation
FastSAM segments the clothing image from background.
Graphonomy Parsing
Graphonomy performs human parsing on the user image.
DensePose Estimation
DensePose estimates the body pose and landmarks.
DCI-VTON Synthesis
Agnostic image + pose + segmented cloth are passed to DCI-VTON.
Result Preview
Final try-on image is generated and returned to the frontend.

Key Features

Photo Upload
Upload user photo or select standard avatar for privacy-conscious users.
Clothing Upload
Upload clothing item image from gallery or camera capture.
AI-Based Fitting
Accurate pose and body parsing for realistic virtual try-on experience.
Privacy-Friendly
Options for hesitant users who don't want to upload real photos.
Cross-Platform
Accessible on Web, iOS, and Android platforms.
Real-Time Processing
Fast AI processing for immediate try-on results.

Impacts & Results

  • Enhanced user engagement in fashion e-commerce
  • Reduced return rates by helping users visualize fit and style
  • Provided an accessible and automated alternative to physical trials
  • Proved feasibility of AI-powered virtual fitting using only open-source tools
  • Promoted inclusivity with avatar-based try-ons for privacy-conscious users
  • Built scalable solution with local infrastructure and efficient processing
  • Cross-platform compatibility for maximum user accessibility
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