GoKawiil8 questions in, 58 Anny body params out. A small MLP trained with a physics-aware loss, runs in milliseconds on CPU. Height accuracy 0.3 cm, mass 0.3 kg, BWH 3-4 cm — better than our photo pipeline on circumferences, without needing a photo. That’s the questionnaire path I promised in the previous post.
The whole story begins with one observation: that height and weight can estimate body measurements quite accurately (Bartol’s regression). The original idea isn’t as accurate as it claims, but after a bit of tuning the results are quite promising.
The questionnaire addresses privacy, speed and cost concerns. Plus we skip the phase where the user spends 5 minutes scrolling for perfect-light, tight-clothes photos. Additionally, it helped us find and address a mass calculation inconsistency in the Anny model, and model the “muscle weighs more” problem.
Backstory
When we want to create a digital twin, we naturally think of HMR photo reconstruction. This route has a lot of ups and downs. During one “down”, the research agent brought up this:
The most striking finding is from Bartol et al. (2022): a simple linear regression from just height + weight (no photo!) predicts 15 body measurements at 1.2-1.6 cm MAE. Many deep learning methods with photos don’t even beat this.
At first I quickly calculated the number of combinations and the number of people, and thought it didn’t make sense. But then, after comparing friends, I thought there might be something to it.
It’s not just height and weight
Intuitively we all know that you can be a man with 178cm and 80kg with a belly, or from the gym. So it wasn’t a surprise that we came up with these two bodies:
They are a bit cartoonish and pushed to extremes, but clearly show the problem.
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