Virtual Try-On for Fashion Brands

Virtual try-on has become one of the most requested capabilities in fashion e-commerce, and for a clear reason: fit is the number one problem online. The average online apparel return rate runs to 24.4%, and size is the single biggest cause at 53% of returns (Coresight Research, 2023). Anything that helps a shopper judge how a garment actually looks and fits before buying attacks that cost directly, which is why 85% of apparel decision-makers say they plan to implement virtual try-on (Coresight, 2023).

But "virtual try-on" is used loosely, and the version a brand needs is often not the one it pictures. There are really two.

Two kinds of virtual try-on

Consumer virtual try-on is the interactive fitting room: a shopper uploads a selfie or picks an avatar, and a widget shows them wearing the item. It is a storefront feature, powered by a real-time try-on engine.

Catalog-side virtual try-on is the production layer underneath: showing every product worn by a realistic model, at catalog scale, so shoppers can judge fit and drape from the imagery itself. No selfie required. This is where most of the return-reducing value actually lives, because it improves the images every shopper sees, not just the ones who opt into a gadget.

On-Model works on the second layer. We are honest about that: On-Model is not a real-time consumer fitting-room widget. It is how brands produce try-on-quality on-model imagery for a whole catalog.

The consumer virtual fitting room

The consumer-facing virtual fitting room is what most people mean by "ai virtual try on." Google Shopping, several marketplaces, and a handful of specialist tools now let shoppers preview clothing on a model or on themselves. It is genuinely useful for hero products and can lift conversion. It is also narrow: it typically covers a subset of items, depends on shopper participation, and does nothing for the thousands of SKUs that never get a bespoke try-on treatment.

In its most literal form, the shopper becomes the model. They photograph themselves, pick a garment, and see it on their own body, with the pose, the lighting, and the room left exactly as they were. Only the clothing changes.

That gap is the opportunity. Baymard Institute's usability research is blunt about it: showing apparel on a human model is essential to purchase confidence (2020). The brands that win are not the ones with a fitting-room gimmick on ten products. They are the ones whose entire catalog reads as "tried on."

Catalog-side try-on: on-model imagery at scale

Getting try-on-quality imagery across a full range used to mean a photoshoot per style, which is why most catalogs are a patchwork of flat-lays, packshots, and the occasional model shot. AI changes the economics. You can take the product photography you already have and turn it into consistent on-model imagery for every SKU.

Three flat-lays become a coherent on-model set on one consistent identity, shot from several angles. Do that across a catalog and every product page communicates fit and drape, the things a shopper most wants to judge, which is exactly what a fitting room is trying to deliver.

Two capabilities make this practical at scale:

• Flat-to-model turns an existing flat-lay or packshot into on-model imagery, with the garment kept pixel-faithful so the image matches what ships.
• Model swap replaces the model in an existing photo while keeping the garment and pose identical, so you can localize a look or refresh a campaign without a reshoot.

Because the models are reusable identities, the same faces run across your whole range. Consistency is what turns a pile of images into a catalog a shopper (and an AI shopping engine) trusts.

"Most brands do not need a try-on widget on ten products. They need every product to look tried on. Catalog-side, that is a solved problem: consistent on-model imagery for every SKU, with the garment kept exact."

— Nunzio Alexandro Letizia, Co-founder at PiktID and creator of On-Model

Where Google Shopping virtual try-on fits

The two layers are converging. Google Shopping's virtual try-on and AI shopping assistants increasingly show shoppers how an item looks worn, and they draw on the imagery and product data brands supply. A catalog with complete, consistent on-model imagery is the one positioned to feed those experiences well. We cover that in making your fashion catalog GEO-ready.

What to do about it

Start with the products that drive returns. Give your best-selling and highest-return styles complete on-model coverage, consistent identities, and multiple views, then work outward across the catalog. You do not need to build a fitting-room widget to capture most of the value. You need imagery that lets every shopper see the garment worn.

For the numbers behind returns, fit, and AI adoption in fashion, see our fashion e-commerce statistics. To turn your existing product shots into on-model imagery across a catalog, start free.

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Sources:

1. Coresight Research. (2023). Alarming Return Rates Require Loss-Minimization Solutions. coresight.com
2. Baymard Institute. Apparel Products Need a Human Model. baymard.com
3. Google. Virtual try-on and AI-powered shopping. blog.google