Ai Based Virtual Try On System Using Dl

Abstract

The Proliferation Of E-commerce Has Fundamentally Altered Consumer Purchasing Behaviour, Yet A Persistent Challenge Remains: The Inability To Physically Try On Garments Before Purchase. This Paper Presents A Virtual Clothing Try-On (VCTO) System That Bridges This Gap By Combining A Custom Conditional Generative Adversarial Network (cGAN) With A Geometric Matching Module (GMM) Embedded In A Full-stack MERN (MongoDB, Express.js, React.js, Node.js) Web Application. The Proposed System Accepts A Reference Person Image And A Desired Garment Image As Inputs And Synthesises A Photorealistic Composite Image By (i) Estimating Human Body Pose Using A Lightweight Keypoint Detector, (ii) Warping The Garment Via Thin-Plate Spline (TPS) Transformation, And (iii) Generating The Final Try-on Image Through An Adversarial Training Scheme. Experimental Evaluation On The VITON-HD Benchmark Dataset Yields A Structural Similarity Index (SSIM) Of 0.873, Fréchet Inception Distance (FID) Of 8.34, And Learned Perceptual Image Patch Similarity (LPIPS) Of 0.072, Outperforming Several Baseline GAN-based Methods. The System Achieves An Average Inference Latency Of 320 Ms On A Single NVIDIA RTX 3060 GPU, Making It Suitable For Near-real-time Web Deployment.

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