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)
- Industry
- Fashion & E-commerce
- Type
- Computer Vision & AI
- Year
- 2024
- Our 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.
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.
What we built.
- featHuman parsing model segmenting body from any background
- featAutomated body measurement estimation (chest, waist, height) from a single photo
- featAI-powered size recommendation (S/M/L/XL) based on extracted measurements
- featGAN-based fabric warping with shadow and fold simulation
- featPoisson blending for photorealistic clothing edge fusion
- featMulti-pose support, accurate beyond strict T-pose
Hard parts.
fixInconsistent user photo lighting and backgrounds degrading results
Built a robust preprocessing stage using OpenCV and DeepLabV3 for background removal and lighting normalization before any model inference.
fixClothing overlays looking like flat stickers on the user's image
Developed a custom Texture Fusion layer using Poisson blending techniques to seamlessly integrate clothing edges with body contours, creating a photorealistic result.
fixHigh computational load causing slow processing for mobile users
Optimized the inference engine using TensorRT and a GPU-accelerated FastAPI backend, reducing per-request processing time by 60%, achieving sub-3-second results.
fixEstimating accurate 3D body measurements from a 2D photo
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.
Built with
Python FastAPI PyTorch OpenCV Detectron2 GANs Docker React Native
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