AI Virtual Trial Room

Revolutionary AI-powered solution using Region Proposal Neural Networks for efficient, cost-effective virtual clothing trials

90%

Accuracy

600+

Dataset Images

Cost-Effective

Solution

Project Overview & Motivation

The rapid growth of e-commerce has transformed retail, with online clothing shopping gaining immense popularity. However, this shift brings challenges including high return rates due to ill-fitting garments and safety concerns with physical trial rooms.

Current Problem

Existing solutions rely on expensive hardware like Microsoft Kinect 2 cameras, costing up to $20,000/year for licensed kiosks.

Our Solution

Cost-effective, AI-powered virtual trial room using advanced computer vision without specialized sensors.

AI-Powered Virtual Trial Experience

Technology Stack

Powered by cutting-edge AI and computer vision technologies:

TensorFlow

Primary framework for building and training deep learning models with robust performance.

Mask R-CNN

Advanced architecture for instance segmentation and precise object detection.

OpenCV

Computer vision library for image processing, resizing, and superimposition.

CAFFE Framework

Custom modified model for accurate key-point detection on bodies and garments.

VGG Annotator

Precision tool for annotating datasets and marking key points and regions.

Methodology

Our innovative six-step process for realistic garment superimposition:

Data Collection & Annotation

Curated custom dataset of 600+ shirt images with diverse styles and sizes, meticulously annotated using VGG Annotator for precise key-points and contours.

Dataset Quality: Each image manually verified for annotation accuracy

View Annotations

Mask R-CNN Training

Architecture Details

Trained Mask R-CNN on annotated dataset for superior instance segmentation, providing pixel-level masks for each Region of Interest (ROI).

40

Training Epochs

90%

Class Accuracy

Ground Truth Generation & Cropping

Generate precise masks that highlight garment portions and crop excess white space for cleaner results.

Original Mask

Generated Ground Truth Mask

Refined Boundaries

Cropped & Refined Result

Key-point Detection

Critical for accurate superimposition - detect key points on both human body (shoulders, neck, waist) and garments using modified CAFFE-based model.

Shoulder Points

Neck Reference

Waist Alignment

Interactive Points

Intelligent Garment Resizing

Dynamic resizing based on user body shape using unitary method calculations for required width (R_W) and height (R_H) based on key-point coordinates.

Mathematical Model

Advanced mathematical model for precise fitting calculations

Transparent Background & Superimposition

Virtual Try-On

Final step combines transparent background processing with precise coordinate geometry for realistic virtual try-on effect.

Result: Seamless garment integration with natural appearance

Results and Findings

Comprehensive evaluation of model performance and processing efficiency:

Performance Metrics

Parameter	Loss	Accuracy
class_loss	0.1092	90%
bbox_loss	0.1897	81%
mask_loss	0.3826	61%

All parameters validated after 40 epochs of Mask R-CNN training

Performance Comparison

Interactive Chart

Runtime efficiency comparison between Mask R-CNN and other pose estimation models

Advantages and Applications

Key Advantages

Cost-Effective Solution

Eliminates expensive Kinect sensors, reducing implementation costs by up to 80%

Enhanced Customer Satisfaction

Visual garment preview reduces returns and improves shopping experience

Enhanced Safety

Safer alternative to physical trial rooms, addressing privacy concerns

Reduced Garment Damage

Minimizes wear and tear from physical try-ons, preserving inventory quality

Greater Accessibility

Makes virtual trial technology accessible to SME vendors

Applications

E-commerce Integration

Seamless integration into online shopping platforms and mobile applications for enhanced user experience.

Interactive Retail Kiosks

Digital mirrors and LCD displays in physical stores for immersive shopping experiences.

AR/VR Future Integration

Expandable to real-time 3D virtual trials using Augmented and Virtual Reality technologies.

Personalized Recommendations

AI-driven suggestions based on user preferences, body type, and style history.

Design Feedback System

User ratings and feedback integration to improve garment designs and quality iteratively.

Project Impact & Future

This project successfully introduces an innovative virtual trial room system that revolutionizes the intersection of AI technology and retail experience. By leveraging the power of Mask R-CNN and advanced key-point detection, we've developed an efficient, cost-effective solution that transforms online shopping.

80%

Cost Reduction

vs. traditional solutions

90%

Model Accuracy

classification performance

∞

Future Potential

scalable applications

Vision for Tomorrow

We believe this system will serve as a robust foundation for future advancements in virtual try-on technology, making personalized, immersive shopping experiences more widespread and affordable. The technology opens doors to AR/VR integration, real-time processing, and AI-driven fashion recommendations that will reshape retail forever.

Open Source Ready

Sustainable Solution

User-Centric Design