  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,
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 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 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.
