  We address the problem of single photo age progression and regression-the
prediction of how a person might look in the future, or how they looked in the
past. Most existing aging methods are limited to changing the texture,
overlooking transformations in head shape that occur during the human aging and
growth process. This limits the applicability of previous methods to aging of
adults to slightly older adults, and application of those methods to photos of
children does not produce quality results. We propose a novel multi-domain
image-to-image generative adversarial network architecture, whose learned
latent space models a continuous bi-directional aging process. The network is
trained on the FFHQ dataset, which we labeled for ages, gender, and semantic
segmentation. Fixed age classes are used as anchors to approximate continuous
age transformation. Our framework can predict a full head portrait for ages
0-70 from a single photo, modifying both texture and shape of the head. We
demonstrate results on a wide variety of photos and datasets, and show
significant improvement over the state of the art.
