To me it sounded really similar to a Generative Adversarial Network (a GAN). With a GAN you have one network whose job is to classify an image (is this picture really a person?) and another whose job is to essentially fool the classifier (generate an image that looks like a person).

This case is a little bit of the reverse, in that it's focused on making the computer vision component (the discriminator) try to match the visual content that has already been generated.

Seems like these types of "adversarial" approaches will be used in lots of different domains, as so far they've produced some pretty amazing results.