Personally I don't think there's any machine learning needed for this (but who knows what they used). If I were to build a replica, my approach would be to grab a decent amount of suitable photos, manually mark the pointed areas in them, and have some sort of scaling/cropping/offset handle near misses.

I upvoted the submission because I think it's a cool trick either way.

It just shows that a "Lot of Prep" can be "machine learning" to the observer.

In the ye olden demoscene days, we called that "precalc".

Just calculate all the effects up front and then draw them "in real time" :)

If it were "lot of prep" - e.g. a manually-tagged and fixed database of about 100,000 images of people pointing (assuming 100 columns and 100 rows in the browser-viewport) then that's still small enough for an instantaneous response from the web-server, so the relative long busy-time suggests that _something_ computationally expensive is running in the background - such as a "this-pointing-finger-does-not-exist"-engine.

It could just be fake busy. Timing is everything in comedy, so give a first time user a little suspense to think advanced computer magic is happening before the punchline arrives.

Exactly. Looking at the JS code (even minified), you can see that it's calling a setTimeout for effect - possibly also waiting for image load-, but all the data is there already.

If it runs instantaneously, it would probably be a pretty cool effect to have the pointing fingers trace your cursor as it moves.

If I had a piece of ML that could do that, I'd just run it once on the server and grab a coffee as it generates a set of 100k labeled images for me.

IIRC, the "this X does not exist" sites also don't run ML on request, but serve cached images. At least that was my impression from getting the same image more than once when I spammed F5 fast enough.

All work and no fun makes hacker a dull person.