WEARX

A Virtual Try-On System with Realistic Cloth Draping

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Overview

WEARX is a virtual try-on system that combines 3D body modeling, realistic cloth draping, and deep learning to create a fast, interactive, and visually accurate try-on experience. The system is designed for e-commerce and AR applications, offering a seamless and engaging user experience.

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Features

• 3D Body Modeling using SMPL for realistic human representation
• Advanced Cloth Draping for natural garment fitting
• Deep Learning Integration for enhanced cloth behavior and realism
• High-Performance Pipeline for interactive virtual try-on
• E-Commerce & AR Ready system for real-world applications

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Technologies Used

• Python
• PyTorch / TensorFlow
• OpenCV
• Blender / Marvelous Designer (Optional for advanced simulations)
• SMPL Model
• Git + GitHub

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Project Structure

WEARX_TRYON/
├── data/                # Data storage (raw, processed)
├── models/              # Pre-trained and custom models
├── src/                 # Source code
│   ├── preprocessing/   # Data preparation modules
│   ├── draping/         # Cloth simulation & fitting logic
│   └── rendering/       # Visualization and output rendering
├── outputs/             # Generated outputs
├── requirements.txt     # Dependencies
└── README.md            # Project documentation

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Getting Started

1. Clone the Repository

git clone https://github.com/prajesdas/WearX_TryOn.git
cd WearX_TryOn

2. Set Up Environment

python -m venv venv
source venv/bin/activate  # For Linux/Mac
venv\Scripts\activate     # For Windows
pip install -r requirements.txt

3. Run the Pipeline

python src/main.py --input_image path/to/image.jpg

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Sample Output

(Add images or GIFs of try-on results here)

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Future Roadmap

• Integration with Augmented Reality (AR) platforms
• Support for additional clothing types and accessories
• GAN-based texture and drape refinement
• Custom cloth parameter editor for advanced customization
• Dataset expansion with high-quality annotated pairs

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Meet the Team

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Contributing

Contributions are welcome! Please open an issue to discuss new features or improvements before submitting a pull request.

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