Check out the first image in the Results section. It's not blending between two images, it's taking one image (in the middle row) and changing different characteristics. It's the understanding of what these characteristics are that's interesting. Taking them all together, they have created a "feature space" which is a multidimensional space where every point corresponds to a different kind of face.
Thank you for the clarification! The latent space is so well behaved that that I can find linear relationship between the latent vector and the feature labels, and then use the regression slop as the "understanding" of the space
