Note: “ask them for the faciest possible thing” seems confused.
How I would’ve interpreted this if I were talking with another ML researcher is “Sample the face at the point of highest probability density in the generative model’s latent space”. For GANs and diffusion models (the models we in fact generate faces with), you can do exactly this by setting the Gaussian latents to zeros, and you will see that the result is a perfectly normal, non-Eldritch human face.
I’m guessing what he has in mind is more like “take a GAN discriminator / image classifier & find the image that maxes out the face logit”, but if so, why is that the relevant operationalization? It doesn’t correspond to how such a model is actually used.
EDIT: Here is what the first looks like for StyleGAN2-ADA.
Yes? Not sure what to say beyond that.
Without saying anything about the obstacles themselves, I’ll make a more meta-level observation: the field of ML has a very specific “taste” for research, such that certain kinds of problems and methods have really high or really low memetic fitness, which tends to make the tails of “impressiveness and volume of research papers, for ex. seen on Twitter” and “absolute progress on bottleneck problems” come apart.