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Project_File // COMPUTER_VISION-VIRTUAL-APPAREL-FITTING

Computer Vision System for Virtual Apparel Fitting & Biometric Sizing_

A GPU-accelerated AI engine extracting body measurements from user photos and applying realistic fabric-warped clothing overlays — powering a virtual fitting room experience that secured further startup funding.

Industry_SectorFashion & E-commerce
Core_ClassificationComputer Vision & AI
Deployment_Year2024
Computer Vision System for Virtual Apparel Fitting & Biometric Sizing

Entity_Client

Fashion-Tech Startup (NDA)

Primary_Role

Lead AI Architect & Vision Engineer

Duration_Log

8 weeks

Resource_Team

1 dev

Project_Overview

A fashion-tech startup wanted to reduce the industry's high return rate by letting shoppers virtually try on clothes from their phone. We built the core AI engine: a body segmentation and measurement extraction system combined with a GAN-based clothing warping and fusion pipeline — designed as a frontend-agnostic API powering their React Native mobile app.

Operational_Process

Implemented a multi-stage computer vision pipeline: background removal and body segmentation with DeepLabV3 → human parsing for body landmark extraction → 2D-to-3D measurement estimation with reference calibration → clothing warping via GAN-based Texture Fusion → GPU-optimized inference with TensorRT → FastAPI serving results to the mobile client.

Core_Capabilities

Human parsing model segmenting body from any background
Automated body measurement estimation (chest, waist, height) from a single photo
AI-powered size recommendation (S/M/L/XL) based on extracted measurements
GAN-based fabric warping with shadow and fold simulation
Poisson blending for photorealistic clothing edge fusion
Multi-pose support — accurate beyond strict T-pose
Reference calibration using phone sensor data or known objects for scale
TensorRT GPU-accelerated inference for sub-3-second try-on results

Performance_Metrics

Processing Time

8+ seconds→<3 seconds

DATA_POINT: 60% reduction

Visual Realism

sticker-like→photorealistic

DATA_POINT: Poisson blending

Try-On Requests

prototype→production-ready

DATA_POINT: thousands/day capacity

Sizing Accuracy

manual guessing→AI-estimated

DATA_POINT: from single photo

Funding Outcome

seed round→secured further funding

DATA_POINT: post-demo

Multi-Pose Support

T-pose only→any natural pose

DATA_POINT: robust detection

Conflict_Resolution

Solution

Built a robust preprocessing stage using OpenCV and DeepLabV3 for background removal and lighting normalization before any model inference.

Resolution_Status: OKProtocol: Direct_Intervention
Solution

Developed a custom Texture Fusion layer using Poisson blending techniques to seamlessly integrate clothing edges with body contours, creating a photorealistic result.

Resolution_Status: OKProtocol: Direct_Intervention
Solution

Optimized the inference engine using TensorRT and a GPU-accelerated FastAPI backend — reducing per-request processing time by 60%, achieving sub-3-second results.

Resolution_Status: OKProtocol: Direct_Intervention
Solution

Designed a Reference Calibration system using phone sensor data or a known object in the frame as a scale reference, enabling accurate real-world measurement estimation from 2D input.

Resolution_Status: OKProtocol: Direct_Intervention