  GAN inversion aims to invert an input image into the latent space of a
pre-trained GAN. Despite the recent advances in GAN inversion, there remain
challenges to mitigate the tradeoff between distortion and editability, i.e.
reconstructing the input image accurately and editing the inverted image with a
small visual quality drop. The recently proposed pivotal tuning model makes
significant progress towards reconstruction and editability, by using a
two-step approach that first inverts the input image into a latent code, called
pivot code, and then alters the generator so that the input image can be
accurately mapped into the pivot code. Here, we show that both reconstruction
and editability can be improved by a proper design of the pivot code. We
present a simple yet effective method, named cycle encoding, for a high-quality
pivot code. The key idea of our method is to progressively train an encoder in
varying spaces according to a cycle scheme: W->W+->W. This training methodology
preserves the properties of both W and W+ spaces, i.e. high editability of W
and low distortion of W+. To further decrease the distortion, we also propose
to refine the pivot code with an optimization-based method, where a
regularization term is introduced to reduce the degradation in editability.
Qualitative and quantitative comparisons to several state-of-the-art methods
demonstrate the superiority of our approach.
