META FIT: Virtual Try-On Series
Building a GAN-based virtual try-on system — from surveying 15+ VTON models and pose estimation to full-body garment transfer and smartphone app design.
5 articles
Part 1: From Photo Booths to Virtual Try-On — The 20-Year Quest
The origin story of META FIT: how a decades-old vision of seeing yourself in clothes before buying evolved from hardware kiosks to GAN-powered virtual try-on, plus a comprehensive survey of 15+ VTON research models.
Part 2: Understanding GANs — The Engine Behind Virtual Try-On
A deep dive into Generative Adversarial Networks: how the generator-discriminator dynamic works, why GANs dominated image generation before diffusion models, and how they power virtual try-on systems.
Part 3: Inside PF-AFN — The Try-On Engine in Code
A code-level walkthrough of the Parser-Free Appearance Flow Network: Feature Pyramid encoding, CUDA-accelerated correlation kernels, optical flow warping, and the ResUnet generator that composites garments onto people.
Part 4: Pose Estimation, Body Measurement, and 3D Reconstruction
How OpenPose skeletal detection, Graphonomy human parsing, and custom body measurement algorithms work together to enable accurate virtual fitting — plus an exploration of PiFu for 2D-to-3D reconstruction.
Part 5: Results, Failure Modes, and the Path to Modern Image Generation
What the GAN-based virtual try-on system achieved, where it failed (and why), the smartphone app design, and how diffusion models are changing everything for the next generation of META FIT.