Generative Virtual Try-On
Multi-stage pipeline - segmentation, pose, dense correspondence, garment parsing, neural refinement.
Why it mattersReduces returns and friction in e-commerce by showing the shopper the garment, not a stock photo. Also a reusable CV stack for any body-aware retail or fitness product.
What it does
A generative computer-vision pipeline that fits a garment to a person's photo - realistically enough to reduce the "is this going to fit me" friction that drives e-commerce returns.
Where it applies
- Fashion e-commerce looking to cut return rates and boost conversion.
- Fitness and healthcare products that need body-aware interfaces (form checks, posture coaching, gear fit).
- Any product that would benefit from seeing the user in context, not in a stock photo.
How it works (high level)
Segmentation and pose extraction locate the body. Dense correspondence mapping aligns the garment to that body. A garment-parsing step preserves fabric semantics. A neural refinement pass blends the composite into something coherent. Each stage is independently evaluable - which matters, because end-to-end metrics on generative CV are famously noisy.
Stack
PyTorch · OpenCV · diffusion models · pose + segmentation models.