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Discovering tech tools that cater specifically to individual preferences is always delightful. This is precisely what happened with a recent feature discovered on Depop, a resale app primarily used for buying. An understated button allows users to visualize outfits by selecting clothing and accessories from different listings.

Overview of the New Depop Feature

Recently, a small scissors icon appeared on the listings during my browsing, situated conveniently above the familiar like and bookmark buttons. Intrigued, I clicked it and was directed to a page entitled My Outfits, where I could easily eliminate the background from the listing images and compile them into a sort of mood board.

Here’s how it operates: Imagine spotting a sweater you adore while shopping, but you’re unsure what bottoms would complement it. Later, you come across some jeans that catch your eye and wonder if they would work well with that sweater. This new functionality enables you to add both items to your My Outfits section, where backgrounds of the images are stripped away, allowing you to drag and arrange the pieces on a blank canvas. This makes it easier to envision how they would pair together before making a purchase. The outfits you create are saved to your account, so you can revisit or modify them at any time. Plus, they are linked back to the original listings, facilitating a smooth transition to the buying process when ready.

Shopping on resale platforms offers the chance to find distinctive items that aren’t worn by everyone—while also enabling one to purchase popular items at reduced prices. Countless times, I had an outfit idea sourced from various listings and struggled to visualize how they would come together in practice. Just this past week, I purchased a top and a pair of pants I thought would mesh well. Unfortunately, they didn’t. Typically, I take screenshots of both items and share them in a text chain with a friend for a rough visual alignment. While it is a basic method, it provided a rough idea of how the items could work together.

Now, with Depop’s innovative feature, crafting outfit visions is boundless. Include shoes, bags, hair accessories, tops, jackets, and pants—even if they originate from different sellers.

Suggested Enhancements

Recently, the Doppl app by Google was reviewed as a tool to help visualize how clothing will look on an individual using AI. Unfortunately, it fell short, as the functionality only allowed users to “try on” one item while the rest of the outfit would be generated by AI—not particularly useful! In contrast, the new Depop feature excels in allowing users to mix and match various pieces. However, it would benefit from incorporating features similar to Doppl, permitting users to dress a digital avatar that showcases their own hair and eye color for a more personalized experience.

A few challenges remain that could affect usability. One significant concern is that the images utilized in the My Outfits feature are simply photos taken by sellers. As a seller, high-quality images aren’t always a priority. Many sellers, including myself, may opt for basic photographs rather than utilizing professional setups. It’s common for some to post dimly-lit images of jeans or shoes without much effort. Consequently, when utilizing My Outfits, the overall aesthetic quality will be heavily influenced by the photos imported; thus, the representation may vary significantly.

Additionally, the ubiquitous challenge of online shopping persists—the fit of garments cannot be accurately gauged until they are actually tried on. Arranging a top above a skirt in the My Outfits section may help with color coordination (assuming decent lighting), but it cannot accurately predict how the fit will feel. Will the skirt ride high or low? Will there be skin exposure? The answer lies with the wearer’s measurements alongside the sizing details provided for each item.

Nevertheless, the feature remains inventive and enjoyable; outfit creation has occupied my week. Friends were informed about it, yet despite updating their applications, they have yet to see the feature, indicating that it may still be rolling out gradually.