There's a lot of interesting discussion here!

I am the ex-CTO and co-founder of a company that does 3D-scan-based fit, specifically ML-based morphology extraction from 3D data of people (www.treedys.com , not a YC company ;-) ).

A lot of the points raised here in the comments are very valid: there is absolutely no consensus on standardisation of "fit"... but even if there was: "fit" is a very complicated concept that means different things to people in different settings. Furthermore there is often no consensus even inside big clothing companies as to which grading system (this is the "fit catalog" so to speak) is to be used for a specific collection / year / style etc... and those companies that do have this kind of thing standardised are often fighting with internal staff that grew up in a non-digital era to keep the documentation up to date, make sure the right files are shared, push people towards using digital design tools consistently etc...

But this does not mean - by any stretch - that the current system cannot be improved! There are many things that can be done with morphological data that are not immediately obvious in terms of improving fit for individual end users. Comparing "tshirt measurements" VS "human measurements" is the obvious starting point, but this is not actually the best way to make real-life recommendations, and there are many better systems that are possible once you have high quality metrics!
Here is one for example: what about completely disregarding the "extracted body measurements in inches", and just building a flywheel where you take people's entire set of say 250 measurements as a vector, and use this data point along with a set of items they have bought (for themselves) and not returned. Once you have this kind of data for, say, 5000 people and 50 SKUs of items of clothing... you can build a very granular recommendation system, based on morphology, which will take into account many aspects of "fit" that are not immediately obvious in the measurements...

One thing is for sure: the big incumbents in fashion retail have felt the sting of shifting to an online-first world. In 2020 the total dollar amount of merchandise returned from online retail doubled (https://nrf.com/media-center/press-releases/428-billion-merc...).
Literally: double the volume in one year. This was especially impactful in online fashion, where returns have always been a giant cost and a limiting factor to expansion ... but up until 2020 fashion retailers had been delaying action while desperately trying to modernise other aspects of their businesses, and morphology data felt like "too big of a problem for now.

Now, in 2021, with a few big players like Amazon taking the lead in "morphology-based fit" the situation is changing... and even the slowest fashion brands are starting to realise that not having a fit / morphology strategy in 2021 is starting to look like an automotive manufacturer NOT having an EV strategy in 2012 when Tesla was releasing the model S...

We're feeling it: after hanging on by the skin of our teeth for 6 years building deep tech solutions ( 3D ML to map a naked body on messy point cloud data of a clothed person for example, that was a tough nut to crack!), we're suddenly getting a lot more inbound from people we could not get on phone for a 5-minute conversation 2 years ago. And we finally have our first public-facing deployments in-store, using the full capture-to-ML stack (all developed in-house) that we have been crafting all this time! It feels good to finally get a product out and start getting the feedback loop going after so long.

All of this to say: congrats to Drapr, They made a great product and their timing was excellent! Let's hope this helps GAP achieve it's sustainability goals and reduce returns and waste, because in the end this will be the major benefit to all of us :) But in any case: morphology-based-fit is now "an idea whose time has come", and we can expect a lot of cool new things to come from this over the next few years!