  The task of age transformation illustrates the change of an individual's
appearance over time. Accurately modeling this complex transformation over an
input facial image is extremely challenging as it requires making convincing
and possibly large changes to facial features and head shape, while still
preserving the input identity. In this work, we present an image-to-image
translation method that learns to directly encode real facial images into the
latent space of a pre-trained unconditional GAN (e.g., StyleGAN) subject to a
given aging shift. We employ a pre-trained age regression network used to
explicitly guide the encoder in generating the latent codes corresponding to
the desired age. In this formulation, our method approaches the continuous
aging process as a regression task between the input age and desired target
age, providing fine-grained control over the generated image. Moreover, unlike
other approaches that operate solely in the latent space using a prior on the
path controlling age, our method learns a more disentangled, non-linear path.
Finally, we demonstrate that the end-to-end nature of our approach, coupled
with the rich semantic latent space of StyleGAN, allows for further editing of
the generated images. Qualitative and quantitative evaluations show the
advantages of our method compared to state-of-the-art approaches.
