I've been working on TryOnfy, an AI-powered virtual try-on platform that lets users preview clothing, hairstyles, and accessories on their own photos. Here's what I've learned building it.
The Problem
Return rates in online fashion are brutal — around 30%. The #1 reason? "It didn't look like I expected." Customers can't visualize how something will actually look on them, so they either don't buy (lost revenue) or buy and return (lost margin).
The AI Pipeline
Our try-on system works in three stages:
1. Body Parsing
We detect pose keypoints and segment the person into regions (face, torso, arms, legs, background). This tells the model where to place the garment and how to deform it.
2. Garment Warping
The target clothing item gets geometrically transformed to match the person's pose.
3. Diffusion-Based Rendering
A fine-tuned diffusion model generates the final composite, handling realistic fabric texture, proper lighting, skin tone preservation, and edge blending.
Challenges We Hit
Speed vs. quality tradeoff: Full diffusion inference is slow. We optimized with model distillation and caching to get results in seconds, not minutes.
Hair try-on is harder than clothing: Hair interacts with face shape, skin tone, and lighting in complex ways.
Accessories need precision: Glasses and jewelry require pixel-level accuracy around facial features.
Results
Users can now upload one photo and try on any item in under 10 seconds. We offer a free tier (10 credits) and a $9.90/month subscription for unlimited access.
Check it out at tryonfy.com if you're curious.
What's Next
We're exploring video try-on, multi-item compositing, and API for e-commerce integrations.
If you're working on similar computer vision problems, I'd love to exchange notes.
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