How does AI virtual try-on work?
The app takes your product image and a shopper’s photo and generates a single new image of that product on that person. It is image generation rather than a 3D model, so there is no per-product asset pipeline to build.
What happens between the click and the result
The shopper supplies a photo. The app pairs it with the product image already on your page and generates a composite in which the product appears on that person. It takes seconds rather than minutes, and the work happens on the app’s servers rather than the shopper’s device, which is why it does not need a powerful phone.
Why this differs from 3D and AR
The older approach builds a 3D model of each product and places it on a live camera feed. That gives real-time movement and suits categories where a piece sits in a predictable place on the body. It also means someone has to produce and maintain a model for every product you sell. Generated try-on skips the model and works from photography, which is why a large catalog can be enabled quickly.
Where generated try-on is weakest
Generation is inference, not measurement. It will not give a shopper a pupillary distance, and it does not replace a size chart on a garment with an unusual cut. Treat it as an answer to whether something looks right on someone, and use size guidance for the separate question of whether it will fit.
What actually decides output quality
Mostly your product photography. A clean, well-lit product image gives the model more to work with than a busy lifestyle shot. If you are evaluating apps, run each one on the same three products and compare the results side by side — that is the only variable that matters, and it is entirely specific to your catalog.
Sources: the public Shopify App Store listing for Looksy. Last checked 27 July 2026.
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