Fashion & E-commerce•Computer Vision & AI•2024

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.

Client

Fashion-Tech Startup (NDA)

Role

Lead AI Architect & Vision Engineer

Timeline

8 weeks

Team

1 dev

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.

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.

Key Features

✓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

Challenges & Solutions

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

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

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

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.

Results

Processing Time

8+ seconds→<3 seconds

60% reduction

Visual Realism

sticker-like→photorealistic

Poisson blending

Try-On Requests

prototype→production-ready

thousands/day capacity

Sizing Accuracy

manual guessing→AI-estimated

from single photo

Funding Outcome

seed round→secured further funding

post-demo

Multi-Pose Support

T-pose only→any natural pose

robust detection

Goals

•Build a production-ready virtual fitting room engine for mobile e-commerce
•Achieve photorealistic clothing overlay quality
•Deliver sub-3-second processing times for mobile user experience
•Enable accurate sizing recommendations from a single user photo

Tech Stack

•Python
•FastAPI
•PyTorch
•OpenCV
•Detectron2
•GANs
•Docker
•React Native

Target Users

•Online fashion shoppers
•Fashion e-commerce retailers
•Personal stylists and fashion consultants

Key Learnings

•Sizing accuracy builds more user trust than visual quality — we prioritized the measurement engine first
•TensorRT optimization is the highest-leverage step for production computer vision performance
•Poisson blending is the differentiator between a gimmick and a genuinely convincing virtual try-on
•Building as a frontend-agnostic API from day one enabled rapid mobile integration without rework

Future Plans

•Implement video try-on with natural fabric movement as the user rotates
•Add multi-item outfit assembly (top + bottom + accessories)
•Integrate direct size-to-purchase flow with e-commerce checkout APIs
•Expand to AR overlay via phone camera for real-time try-on