Skip to main content
The Keyword

Virtually try on clothes with a new AI shopping feature

Three photos of models wearing different clothes. The images are overlaid with an icon that says “Try-on models.”

When you try on clothes in a store, you can immediately tell if they’re right for you. And if they’re not, a sales associate can swap them out for pieces with different colors, styles or price points that better match what you’re looking for. Before you know it, you’re leaving with an outfit you love.

You should feel just as confident shopping for clothes online. So today we’re introducing two new features that bring this fitting room experience to you: Virtual try-on for apparel uses generative AI to show you clothes on a wide selection of real models, while new filters help you find exactly what you’re looking for.

See clothes on a range of skin tones and body types

While apparel is one of the most-searched shopping categories, most online shoppers agree: It’s hard to know what clothes will look like on you before you buy them. Forty-two percent of online shoppers don’t feel represented by images of models, and fifty-nine percent feel dissatisfied with an item they shopped for online because it looked different on them than expected.1 Now, thanks to our new virtual try-on tool on Search, you can see whether a piece is right for you before you buy it.

Virtual try-on for apparel shows you how clothes look on a variety of real models. Here’s how it works: Our new generative AI model can take just one clothing image and accurately reflect how it would drape, fold, cling, stretch and form wrinkles and shadows on a diverse set of real models in various poses. We selected people ranging in sizes XXS-4XL representing different skin tones (using the Monk Skin Tone Scale as a guide), body shapes, ethnicities and hair types.

A phone animation shows Google’s virtual try-on feature. The phone shows models in varying sizes wearing a green top.

Our virtual try-on tool shows tops from brands like Everlane on real models.

Starting today, U.S. shoppers can virtually try on women’s tops from brands across Google, including Anthropologie, Everlane, H&M and LOFT. Just tap products with the “Try On” badge on Search and select the model that resonates most with you.

Working alongside our Shopping Graph, the world’s most comprehensive data set of products and sellers, this technology can scale to more brands and items over time. Look out for more options coming to virtual try-on for apparel, including men’s tops launching later this year.

A grid featuring 80 models of varying shapes and sizes, all wearing black clothing.

Our virtual try-on tool features real models.

Refine a product to find exactly what you want

Do you like that top, but want a less pricey version? Or this jacket, but in a different pattern? Associates can help with this in a store, suggesting and finding other options based on what you’ve already tried on. Now you can get that extra hand when you shop for clothes online.

Our new guided refinements can help U.S. shoppers fine-tune products until you find the perfect piece. Thanks to machine learning and new visual matching algorithms, you can refine using inputs like color, style and pattern. And unlike shopping in a store, you’re not limited to one retailer: You’ll see options from stores across the web. You can find this feature, available for tops to start, right within product listings.

A GIF of a mobile screen scrolling through pink blouses. A cursor taps on various refinements, including price, color and pattern.

We’re introducing a new way to refine the shopping results you see.

We believe that AI will continue to improve our lives in ways big and small, including making everyday activities like shopping just a bit more helpful (and fun). Stay tuned for more ways we’re using advanced technology like AI to help you shop online with confidence.


More Information


1

Source: Google/Ipsos, Online shoppers survey, US, 2023, n=1614 of adults age 18 or older who have shopped for clothing online, 4/28/23-4/30/23