Then, I thought this would stop at using the U-space vectors (the ones that form the basis of the PCA image) and their corresponding ellipse to form our color space, but no, the function fitting is a very slick idea, even if it was executed by hand.

Lastly, I love the presentation of sampling from different r values. Whether you sample from a fixed r value or a range of r values, I bet this has great applications in game design or animation.

I still don't quite get the manual data labeling process at the beginning? It seems like it would encode some bias, but the consistency of the first point cloud and the results certainly speak for themselves.

"Only realistic colors" would be a different problem. And I'm not sure if there's a way to solve that problem without excluding some real colors too.

(Maybe if the colour selector was a different shape? I don't know much about color spaces.)

Wish I had this on hand like this three or four years ago when I was working on VR avatar software - we had hard-coded skin tone options at the time - would have been a cool addition!

It might be a bit biased though, as there's much more variation on fairer skin tones than on dark ones (there's only one that looks like the colloquial "black").

This contradicts all research and social media from the past decade.