I think "generated" may be too strong a word for what this system seems to be doing. It feels more like it has found a bunch of averages among a large enough set of similar pictures of some fairly homogenous celebrities. When enough of those averages are put on a gradient along a particular axis you get transformations that seem magically smooth. In the example you gave I think it didn't have enough source photos along that axis. I'll further guess that this works best for celebrities that are generally considered attractive because attractive faces are already averages.
The point is that the image has been transformed into a latent space encoding and interpolating those latent-space variables creates those mind-blowing effects (imagine just moving from one value to another via a simple convex combination). How are those latent variables constructed and what do they represent (or if they could be described easily) is completely up to non-linear optimization process running on a huge number of dimensions.
I don't understand this as the image being transformed, "space" and "dimensions" seem to originate not with the visual features of the images but with the attributes that the CelebA image set is annotated with. The coverage of the set is not uniform with respect to gender and skin color, I'm not 100% sure how to interpret the attribute values but a some quick math shows that images with "Pale_Skin" outnumber their opposites by about 20 to 1.
[1] https://www.ncbi.nlm.nih.gov/pubmed/15376799 "Images of faces manipulated to make their shapes closer to the average are perceived as more attractive."