  Generative Adversarial Networks (GANs) have established themselves as a
prevalent approach to image synthesis. Of these, StyleGAN offers a fascinating
case study, owing to its remarkable visual quality and an ability to support a
large array of downstream tasks. This state-of-the-art report covers the
StyleGAN architecture, and the ways it has been employed since its conception,
while also analyzing its severe limitations. It aims to be of use for both
newcomers, who wish to get a grasp of the field, and for more experienced
readers that might benefit from seeing current research trends and existing
tools laid out. Among StyleGAN's most interesting aspects is its learned latent
space. Despite being learned with no supervision, it is surprisingly
well-behaved and remarkably disentangled. Combined with StyleGAN's visual
quality, these properties gave rise to unparalleled editing capabilities.
However, the control offered by StyleGAN is inherently limited to the
generator's learned distribution, and can only be applied to images generated
by StyleGAN itself. Seeking to bring StyleGAN's latent control to real-world
scenarios, the study of GAN inversion and latent space embedding has quickly
gained in popularity. Meanwhile, this same study has helped shed light on the
inner workings and limitations of StyleGAN. We map out StyleGAN's impressive
story through these investigations, and discuss the details that have made
StyleGAN the go-to generator. We further elaborate on the visual priors
StyleGAN constructs, and discuss their use in downstream discriminative tasks.
Looking forward, we point out StyleGAN's limitations and speculate on current
trends and promising directions for future research, such as task and target
specific fine-tuning.
