  One of the main motivations for training high quality image generative models
is their potential use as tools for image manipulation. Recently, generative
adversarial networks (GANs) have been able to generate images of remarkable
quality. Unfortunately, adversarially-trained unconditional generator networks
have not been successful as image priors. One of the main requirements for a
network to act as a generative image prior, is being able to generate every
possible image from the target distribution. Adversarial learning often
experiences mode-collapse, which manifests in generators that cannot generate
some modes of the target distribution. Another requirement often not satisfied
is invertibility i.e. having an efficient way of finding a valid input latent
code given a required output image. In this work, we show that differently from
earlier GANs, the very recently proposed style-generators are quite easy to
invert. We use this important observation to propose style generators as
general purpose image priors. We show that style generators outperform other
GANs as well as Deep Image Prior as priors for image enhancement tasks. The
latent space spanned by style-generators satisfies linear identity-pose
relations. The latent space linearity, combined with invertibility, allows us
to animate still facial images without supervision. Extensive experiments are
performed to support the main contributions of this paper.
