  Existing approaches and datasets for face aging produce results skewed
towards the mean, with individual variations and expression wrinkles often
invisible or overlooked in favor of global patterns such as the fattening of
the face. Moreover, they offer little to no control over the way the faces are
aged and can difficultly be scaled to large images, thus preventing their usage
in many real-world applications. To address these limitations, we present an
approach to change the appearance of a high-resolution image using
ethnicity-specific aging information and weak spatial supervision to guide the
aging process. We demonstrate the advantage of our proposed method in terms of
quality, control, and how it can be used on high-definition images while
limiting the computational overhead.
