This is interesting -- I can almost recognize the various Pixar characters these are influenced by. For instance, one has the distinct jaw of the old man from "up".
With real human faces it's almost impossible to tell but with these you can definitely pick a character per feature.
So in the end this technology might not be as "liberating" as people think it is.
In a way, this is a much better setup for artists and creatives. There isn't some giant licensing firm controlling your work. You simply buy or rent the best tools to make your work.
That said, it'll only be good for creatives and consumers if there is sufficient competition. And open source equivalents that still enable creation.
It's striking that some of these examples have distinct features from specific, identifiable datasets: we can occasionally recognize specific characters (the old man from Pixar's UP is getting mentioned a lot), but it also reproduces more general aesthetic patterns. Even when I can't recognize the source data, I can distinctly see in some of these faces "the Pixar look", and in others "the DreamWorks look".
Were I an IP lawyer, I would start thinking of arguments along the lines of "this technology simply obfuscates the source of plagiarisms". I would also start to think about trying to force anyone who uses this technology to disclose the sources of their training data, since a model trained largely on "the Pixar look" could be benefiting from Pixar's character design processes without having to hire any of Pixar's artists.
And, if I were philosophically inclined, I would also start thinking about how this is any different from hiring a random artist and instructing them to "design characters that look like Pixar characters".
I suspect that one key difference is that the human artist's success can't easily be measured, but the GAN's success can very easily be measured.