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GAN inversion aims to invert an input image into the latent space of apre-trained GAN. Despite the recent advances in GAN inversion, there remainchallenges to mitigate the tradeoff between distortion and editability, i.e.reconstructing the input image accurately and editing the inverted image with asmall visual quality drop. The recently proposed pivotal tuning model makessignificant progress towards reconstruction and editability, by using atwo-step approach that first inverts the input image into a latent code, calledpivot code, and then alters the generator so that the input image can beaccurately mapped into the pivot code. Here, we show that both reconstructionand editability can be improved by a proper design of the pivot code. Wepresent a simple yet effective method, named cycle encoding, for a high-qualitypivot code. The key idea of our method is to progressively train an encoder invarying spaces according to a cycle scheme: W->W+->W. This training methodologypreserves the properties of both W and W+ spaces, i.e. high editability of Wand low distortion of W+. To further decrease the distortion, we also proposeto refine the pivot code with an optimization-based method, where aregularization term is introduced to reduce the degradation in editability.Qualitative and quantitative comparisons to several state-of-the-art methodsdemonstrate the superiority of our approach.

Cycle Encoding of a StyleGAN Encoder for Improved Reconstruction and Editability