A common view about GANs is that they are heavy computationally demanding models with weird and untrackable optimization process that somehow manage to generate realistic images with significantly lower diversity than original data and are mostly useful to make some kind of image editing.

This post will discuss several recent articles (on layout and keypoints prediction, novel view synthesis, creation of synthetic datasets and labels) that challenge the last point of this view.