  Motivated by the following two observations: 1) people are aging differently
under different conditions for changeable facial attributes, e.g., skin color
may become darker when working outside, and 2) it needs to keep some unchanged
facial attributes during the aging process, e.g., race and gender, we propose a
controllable face aging method via attribute disentanglement generative
adversarial network. To offer fine control over the synthesized face images,
first, an individual embedding of the face is directly learned from an image
that contains the desired facial attribute. Second, since the image may contain
other unwanted attributes, an attribute disentanglement network is used to
separate the individual embedding and learn the common embedding that contains
information about the face attribute (e.g., race). With the common embedding,
we can manipulate the generated face image with the desired attribute in an
explicit manner. Experimental results on two common benchmarks demonstrate that
our proposed generator achieves comparable performance on the aging effect with
state-of-the-art baselines while gaining more flexibility for attribute
control. Code is available at supplementary material.
