Who hasn’t dealt with the frustration of hauling four different sizes to the dressing room because of ridiculous discrepancies in fit from brand to brand? Probably 99.9% of us have dealt with this maddening reality. Now, a New York-based company called Body Labs hopes to make that a distant memory — by commercializing its cutting-edge 3-D body-modeling technology (which is an advanced upgrade on mere 3-D body scanning). Earlier this month, Body Labs raised $8 million in Series A funding (so get ready to hear more about its body-modeling wizardry in the near future). The company began in the early aughts at Brown University’s computer vision lab; then, it continued at Germany’s Max Planck Institute. Though the company doesn’t exclusively address the fashion industry, it has been a focus since Body Labs' inception: “When research started in the early 2000s, e-commerce was just booming, but no one knew how to handle the returns problem. People were suddenly not trying things on before buying them,” says Flo McDavid, director of business development at Body Labs. Brands therefore started having a harder time sussing out how specific designs fit customers’ bodies. When Body Labs was founded in 2013, there was an “input problem” in terms of being able to get the necessary quantities of data, because people didn’t own body scanners, and the scanners on the market were “massive” and “cost tens of thousands of dollars,” McDavid says. So Body Labs worked with entities that could afford to buy huge body scanners to fine-tune their body-modeling fit abilities. (It also worked with the U.S. Army to design Kevlar vests specifically for women.) In just two brief years, technology has changed markedly and 3-D sensors have become very compact and affordable. Body Labs’ clients today include apparel and e-commerce brands ranging in scale from Fortune 500 companies to start-ups in their formative phases.
The typical fit process for any given garment might consist of a designer sending a pattern of a particular piece to a factory overseas for the sample to get made; then, the sample gets sent back to the designer to be tried on a live fit model. That back-and-forth might happen five or six times, over the course of months, before the garment is just right.
By comparison, a designer could use Body Labs to virtually simulate how a piece will fit on a body of any proportions — be it a brand’s sample-size specs or, perhaps, the dimensions of an actual woman — before anything gets physically created, McDavid explains. The technology can also simulate how a particular garment will move on the body, helping to figure out if things will annoyingly bunch or ride up.
Besides accelerating the fit process for designers, Body Labs uses its technology to compare the same size across labels, creating 3-D body models using each brand’s waist, bust, and hip measurements. For example, the contrast between a size 8 at Zara versus Banana Republic is two inches in the chest, three inches in the waist, and two inches in the hips (Zara’s sizing is the slimmer of the two brands in all of those dimensions, unsurprisingly). Some brands even have conflicting sizing within their own product offerings: “Herve Leger’s size chart contradicts itself," McDavid points out. "First it says size medium is 8-10, then it says it’s 6-8; which is it?” There’s also some pretty key information missing from traditional fit metrics, McDavid adds: “Brands...rarely talk about how tall people are, which obviously changes fit a lot. Sometimes, you only get two body measurements — usually waist and hip — to know what size to get.”
So, can retailers make use of Body Lab’s technology to actually, you know, make clothes fit better? The problem, according to McDavid, is that “fit models are often chosen arbitrarily, and don’t actually reflect [a brand’s] average consumer’s body, let alone the range of shapes that exist within a target market. The average woman’s body in New York is not the same as Texas, or Tokyo.” The solution: taking “a lot” of 3-D scans of a particular population (say, everyone who bought a size medium at a particular retailer) and then creating 3-D body models for that specific group, McDavid says, so the retailer can know what a size-medium body truly looks like among that customer base. Expect to see 3-D modeling integrated into your shopping experiences in the next two years. “Retail settings are perfect for scanning: You’re already changing in and out of clothes, and you have a private space,” McDavid says. “Adding in a quick spin in front of a tablet or stepping inside a booth lined with 3-D sensors is easy.” That data could translate to a souped-up e-comm experience with the same retailer, since your digital body model could by used by the retailer to customize the threads you’re browsing based on fit predictions (and inventory). “It deepens the connection you have with a brand that actually understands your body; it makes it a conversation,” McDavid says. Having a strong relationship to a particular fashion label because its clothes really fit you — that’s precisely what retailers’ most profitable dreams are made of.