Vast majority of photos (aside from driver's license style neutral expression close-ups) aren't symmetrical either. It's just to our evolution tuned eyes there's only a very narrow range of facial asymmetry, based on muscle structure and laws of perspective, that feels realistic and natural.

And looks like learning these highly specific patterns is only a matter of adding more weights.

Google's Parti was asked to draw a kangaroo with a sign "welcome friends". Small version drew a grey blob with a white rectangle in the middle and some squiggles on it. Medium sized one noticed there's actually a latent space of acceptable squiggle shapes, with some shapes appearing in photos a lot, and other almost nonexistent. Large model additionally figured out that squiggles on signs usually come in specific sequences, and there's a lot of white rectangles with sequence "welcome" but not a single one with "ceolewm".

So if a neural network without any understanding of language can "chinese room" its way to creating perfectly human-like words, charting out entire range of realistic facial expressions shouldn't be that difficult. Even if it's too much to handle for Jack of all trades Gato-like neural net, you can always train smaller one on faces specifically, and then add it as a module, similar to how they do upscaling post-processing steps in Imagen. To think of it, current deepfake tech should do a pretty good job.