Virtual Try-On System

Introduction

The Virtual Try-On System is an advanced AI-powered solution that enables users to visualize clothing items on themselves digitally. Using Generative Adversarial Networks (GANs), human parsing, and garment warping, the system processes two images:

1. Person Image - An image of a person wearing an outfit.
2. Garment Image - An image of a different outfit only.

The system then generates an output image where the person appears to be wearing the new outfit.

This technology is highly relevant for e-commerce, fashion design, and augmented reality (AR) applications, as it enhances customer experience, reduces return rates, and provides scalable solutions for retailers.

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Table of Contents

• Project Scope & Importance
• Key Uses & Components
• System Architecture
• Project Structure
• Installation
• Usage
  • Training
  • Inference
  • Deployment
• Component Details
• Troubleshooting
• Future Enhancements
• License
• Contact

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Project Scope & Importance

Scope

• End-to-End AI Pipeline - Covers data preprocessing, model training, inference, and real-time deployment.
• Advanced Deep Learning Techniques - Implements GANs for image synthesis, TPS warping for garment alignment, and ResNet-based human parsing.
• Scalability - Optimized for GPU acceleration and cloud deployment.

Importance

• E-Commerce Impact - Reduces return rates by 30-50% by allowing customers to try clothing virtually.
• Cost Efficiency - Eliminates logistical costs of physical try-ons and sample production.
• Innovation - Provides a digital platform for fashion designers to prototype their outfits virtually.

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Key Uses & Components

Key Applications

• E-Commerce Platforms - Integrates with online stores for virtual dressing rooms.
• Fashion Design Studios - Enables rapid prototyping of new designs on digital models.
• Augmented Reality (AR) & Virtual Reality (VR) - Can be extended to mobile/AR glasses for in-store experiences.

Core Components

1. Human Parsing Module - Segments body regions (skin, hair, clothes) using a ResNet-based segmentation model.
2. Garment Warping Module - Aligns clothing to body pose using Thin Plate Spline (TPS) transformation.
3. GAN Generator - A U-Net architecture that synthesizes the final try-on image.
4. PatchGAN Discriminator - Ensures image realism by evaluating synthetic outputs against real ones.
5. FastAPI Backend - Enables real-time image processing and serving through a web API.

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System Architecture

The Virtual Try-On system follows a structured workflow:

1. Image Preprocessing - Resizes images to 256x256 and normalizes them.
2. Human Parsing - A segmentation model isolates body parts to facilitate garment placement.
3. Garment Warping - Uses TPS transformations to warp the clothing image based on keypoints.
4. Image Synthesis - The GAN generator fuses parsed human features and warped garments.
5. Adversarial Training - The PatchGAN discriminator validates the realism of the generated image.
6. Post-Processing - Converts tensors to RGB images and applies denormalization.

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Project Structure

Virtual-TryON-System/
├── model/                   # Neural network definitions
│   ├── generator.py         # U-Net generator
│   ├── discriminator.py     # PatchGAN discriminator
│   ├── parsing.py           # ResNet-based human segmentation
│   └── warping.py           # TPS transformation logic
├── scripts/                 # Deployment and inference scripts
│   ├── deploy.py            # FastAPI server
│   └── inference.py         # CLI for batch processing
├── src/                     # Core logic
│   ├── train.py             # Training loop
│   ├── data_preprocessing.py# Image augmentation and normalization
│   └── evaluation.py        # Evaluation metrics
├── static/                  # Web UI assets
│   ├── index.html           # Frontend interface
│   └── style.css            # Styling
├── uploads/                 # User-uploaded images
├── utils/                   # Helper functions
│   ├── data_loader.py       # PyTorch Dataset class
│   └── visualization.py     # Plot training curves
├── requirements.txt         # Python dependencies
└── README.md                # Project documentation

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Installation

Prerequisites

• Python 3.8+
• NVIDIA GPU (CUDA 11.x recommended)
• Poetry (optional for dependency management)

Steps

git clone https://github.com/FAbdullah17/TryOn_GAN.git
cd Virtual TryOn System
pip install -r requirements.txt

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Usage

Training

python src/train.py --dataset ./data/train --epochs 50 --batch_size 16

Inference

python scripts/inference.py --person ./uploads/user.jpg --clothing ./data/garment.png

Deployment

uvicorn scripts.deploy:app --host 0.0.0.0 --port 8000

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Troubleshooting

Issue	Solution
CUDA Out of Memory	Reduce batch size or enable mixed precision
Keypoint Errors	Ensure 20 keypoints are normalized (0-1 range)
Slow Inference	Optimize model with TensorRT
Blurry Outputs	Increase L1 loss weight in training

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Future Enhancements

1. Automatic Keypoint Detection - Integrate OpenPose for improved accuracy.
2. High-Resolution Outputs - Train on 512x512 images using progressive GANs.
3. 3D Garment Modeling - Enhance realism with depth-aware warping.
4. Multi-Garment Try-On - Allow layering (e.g., shirts over jackets).
5. Mobile Optimization - Convert models to ONNX/TFLite for mobile deployment.

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License

This project is licensed under the MIT License. See LICENSE for details.

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Contact

For inquiries or collaborations:

• Email: fahadai.co@gmail.com
• GitHub: FAbdullah17
• LinkedIn: Fahad Abdullah

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