It takes more like 10 seconds. For a large range of height and weight inputs crossed with all option combinations, you could precompute ~10M measurements and return results basically instantly.
Yeah, the demo wasn't prepared for such peak. Normally it's <2s after warm-up. Like the precomputation idea, but for now it changes too dynamically to precompute each time.
Hey, I'm cofounder.
Overall relevant summary, but here is extra context:
ai;dr - neither of us are english natives. We use AI, but the content and research is ours and AI could not do it well enough. It's n'th iteration. In fact, we started with photo-based approach and over time simplified it to single questionnaire which yields better results. simple != easy != obvious
why: no fancy equipment, no weird camera scanning (yielding bad results anyways). Still plenty of opportunities to make it even simpler. Ideally we'd measure full body as well or better than a tailor with a tape measure in just few minutes. Possibly without tape measure. We're still long way off both in tech and UX, but good enough for market validation
errors:
all of that is roughly within the errors that you'd get from tape measurements. If it's could enough for bespoke, it's good enough to tell you that your bicep won't feel comfortable inthe sleeve
I'm guessing the writing is AI-assisted (there's no fluidity and it has some weirdly placed phrases) but I see they're in Poland and likely not English-language first?
For the humour impaired, pocketsize in women's clothing is contentious because for reasons determined by the fashion police pockets are unsightly and either fictional, or tiny.
One time, I found women's jeans with tiny pockets but the bottom seam could be pulled undone to reveal a man size pocket of cloth below. That was classy.
Wrong. The article is our own research. Writing is claude assited, cuz we are not english natives. As for the code it's also Claude, but once again, LLMs are surprisingly bad at 3D ATM
Interesting idea. Using a questionnaire as input for an MLP makes sense but the real challenge is designing questions that capture useful signal instead of noise. If that part is done well, the approach has a lot of potential.
I don't understand why the height and weight errors aren't 0 when they are known inputs? If I say how tall I am, why is the model estimating something else?
That's a common phenomenon in model fitting, depending on the type of model. In both old school regression and neural networks, the fitted model does not distinguish between specific training examples and other inputs. So specific input-output pairs from the training data don't get special privilege. In fact it's often a good thing that models don't just memorize inputt-output pairs from training, because that allows them to smooth over uncaptured sources of variation such as people all being slightly different as well as measurement error.
In this case they had to customize the model fitting to try to get the error closer to zero specifically on those attributes.
Twenty or so years ago, Levis had a program called Personal Pair (they paired with a firm/product called Intellifit) where you would step into a millimeter wave 3D scanner and get your precise measurements.
Which you could use to get a custom pair of jeans made just for you. This failed partly because the target market turned out to be middle-aged adults (that buy fewer pairs), and not 18-25 year olds like they were hoping. Partly over privacy concerns. Partly the ability of the factory to make jeans to that tight of a tolerance. And partly because it was promoted like it was a novelty ("Consumers love it!"). Mentioned here previously:
But with this software - the tolerances are looser, so the clothing becomes more manufacturable. And the measurements can be anonymous - you don't feel like you're stepping into a TSA scanner for everyone to see.
I hope they are able to make relationships with multiple clothing brands so shopping from home will become less hit-or-miss. The benefit to the brands is going to be fewer returns for size issues.
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[ 2.8 ms ] story [ 64.2 ms ] threadMLP trained on 8 questions achieves ~0.3cm height error, ~0.3kg weight error, and ~3-4cm for bust/waist/hips measurements.
https://www.mdpi.com/1424-8220/22/5/1885 + some hacking => "we want to productize this"
But yeah, it's reasonable to obfuscate that fact form the user
why: no fancy equipment, no weird camera scanning (yielding bad results anyways). Still plenty of opportunities to make it even simpler. Ideally we'd measure full body as well or better than a tailor with a tape measure in just few minutes. Possibly without tape measure. We're still long way off both in tech and UX, but good enough for market validation
errors: all of that is roughly within the errors that you'd get from tape measurements. If it's could enough for bespoke, it's good enough to tell you that your bicep won't feel comfortable inthe sleeve
One time, I found women's jeans with tiny pockets but the bottom seam could be pulled undone to reveal a man size pocket of cloth below. That was classy.
This is definitely manipulated.
> Averages lie about the tails, and a person who gets a 15 cm bust error doesn’t care that the mean is 4 cm.
A variation of that sentence should be mandatory in every scientific paper.
In this case they had to customize the model fitting to try to get the error closer to zero specifically on those attributes.
https://www.youtube.com/watch?v=G1IceHjADbQ
Which you could use to get a custom pair of jeans made just for you. This failed partly because the target market turned out to be middle-aged adults (that buy fewer pairs), and not 18-25 year olds like they were hoping. Partly over privacy concerns. Partly the ability of the factory to make jeans to that tight of a tolerance. And partly because it was promoted like it was a novelty ("Consumers love it!"). Mentioned here previously:
https://news.ycombinator.com/item?id=2444319
But with this software - the tolerances are looser, so the clothing becomes more manufacturable. And the measurements can be anonymous - you don't feel like you're stepping into a TSA scanner for everyone to see.
I hope they are able to make relationships with multiple clothing brands so shopping from home will become less hit-or-miss. The benefit to the brands is going to be fewer returns for size issues.