Use case · Virtual try-on

Shoppers see it on themselves before they buy.

Virtual try-on as a commerce experience, built on the Uwear API: one shopper photo, your products, and generated fits that shop like product pages, inside your app or storefront.

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Case study · Dailyfit

Dailyfit app: today's generated fit on the shopper with shoppable product cards and prices below

Generated on the shopper, not a model
One photo becomes their avatar: a reusable try-on model. Every fit is rendered on them, in a real scene.
Shoppable, straight from the fit
Each look carries its product cards, retailer and price included, one tap from the image.
Personal by design
Taste picks, size, and even the shopper’s colour season shape what gets suggested next.

Screens from Dailyfit, a consumer app that runs its virtual try-on on the Uwear API.

Built on the API

One API. Your shopper's whole try-on flow.

There is no private try-on product behind this: the whole experience runs through the same public API you would wire into your own app or storefront. Four calls, end to end:

POST api.uwear.ai/avatar

create the try-on model

{
  "avatar_name": "shopper_18420",
  "avatar_url": "https://yourapp.com/uploads/shopper.jpg",
  "avatar_enhancement": true
}

POST api.uwear.ai/generation

try-on request

{
  "avatar_id": 4412,          // the shopper's avatar
  "clothing_item_id": 18294,  // your product
  "model_slug": "nano-banana-2",
  "prompt": "walking through a snowy street",
  "num_images": 1,
  "webhook_url": "https://yourapp.com/webhooks/uwear"
}

WEBHOOK yourapp.com/webhooks/uwear

generation.completed

{
  "event": "generation.completed",
  "data": {
    "generation_id": 88213,
    "results": [{
      "generation_result_id": 316482,
      "image_url": "https://…/fit-18294.png"
    }]
  }
}

POST api.uwear.ai/generation

the loop continues

{
  "avatar_id": 4412,   // same shopper
  "outfit_id": 921,    // a saved fit she loved
  "model_slug": "nano-banana-2",
  "prompt": "date-night look, golden hour terrace",
  "num_images": 1
}

Inside the case study

How Dailyfit turned try-on into commerce.

Colour on the customer, not the pitch: the product choices one team made on top of the API, and a sense of what you could build on the same surface.

One photo in

Their shoppers are saved as Uwear avatars: a persistent try-on model from a single photo. No scan, no AR rig.

Picks teach taste

Dailyfit’s this-or-that rounds learn taste app-side, then decide what gets generated through the API.

The fit, on them

Each day the app requests a full outfit generated on the shopper’s avatar, in a real scene.

The full Dailyfit result screen: a generated fit with its shoppable product cards, retailers, and prices

Shop from the look

They attach product cards, retailers, and prices to every generated fit: image to checkout.

How you run it

Design in the Studio. Ship on the API.

Operator interface

Studio

Prototype the looks and scenes by hand before wiring them into your app.

Explore Studio

Programmatic

API

The integration surface for this whole use case: shopper photo in, styled fit out.

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Conversational

Agent Mode

Explore what your try-on experience could generate before you build it.

Explore Agent Mode

In your own tools

MCP

Trigger try-on generations from Claude or any MCP client while you design the flow.

Explore MCP

Why it pays

Confidence before the buy.

A shopper who has seen the garment on their own body buys with more confidence and returns less. These are the pieces that make it production-safe:

The integration

API

Try-on models, generations, and webhooks: the same public API Dailyfit runs on.

Explore API

The trust

Review & QA

Automatic checks on garment fidelity before an image ever reaches a shopper.

Explore Review & QA

The catalog

Asset Library

Your garments and outfits, saved once and try-on ready for every request.

Explore Asset Library

Virtual try-on FAQ

How AI virtual try-on works as a commerce experience on the Uwear platform.

Generating a photorealistic image of a real shopper wearing a product, from one photo of the shopper plus your existing product assets. No 3D scan, body measurements, or AR session required.

AR overlays a garment on a live camera feed, which tends to break on fit and fabric. Uwear generates a complete photograph of the shopper wearing the product in a real scene, using the same generation engine that produces catalog imagery.

Yes. The same inputs as the rest of the platform: packshots, flat-lays, or supplier photos. If your products are already in your Uwear Asset Library, they are try-on ready.

It is an API integration: save the shopper as an avatar from one photo, request generations with your garment or outfit IDs and an optional scene prompt, and receive results by webhook. Your app owns the experience around it.

Dailyfit is a consumer app that uses the Uwear API for its virtual try-on capabilities. We reference it because it shows what a shipped, commerce-grade try-on experience looks like on this platform.

When shoppers have seen a garment on their own body before buying, the gap between expectation and delivery shrinks, which is the main driver of fit-related returns. See our reduce clothing returns page for how teams approach this.