A nuance that is underappreciated by a lot of people / the media is the degree to which any of the AI generates X models (faces, drawings, fills in photos, increases sharpness, etc.) simply copy and paste and interpolate from the training set. Unlike general supervised learning problems, for many of the deep learning "generative" models that get posted to HN regularly, there is no objective "test set" to measure gene…
A different problem I see with faces in particular though is that our visual system is actually wired to do some really heavy denoising/pattern matching on faces (for example people seeing the face of Jesus on slices of toast), so the generation of faces doesn't actually need to be that good to produce results that seem appealing to humans.