  Face age editing has become a crucial task in film post-production, and is
also becoming popular for general purpose photography. Recently, adversarial
training has produced some of the most visually impressive results for image
manipulation, including the face aging/de-aging task. In spite of considerable
progress, current methods often present visual artifacts and can only deal with
low-resolution images. In order to achieve aging/de-aging with the high quality
and robustness necessary for wider use, these problems need to be addressed.
This is the goal of the present work. We present an encoder-decoder
architecture for face age editing. The core idea of our network is to create
both a latent space containing the face identity, and a feature modulation
layer corresponding to the age of the individual. We then combine these two
elements to produce an output image of the person with a desired target age.
Our architecture is greatly simplified with respect to other approaches, and
allows for continuous age editing on high resolution images in a single unified
model.
