  Conventional methods for facial age analysis tend to utilize accurate age
labels in a supervised way. However, existing age datasets lies in a limited
range of ages, leading to a long-tailed distribution. To alleviate the problem,
this paper proposes a Universal Variational Aging (UVA) framework to formulate
facial age priors in a disentangling manner. Benefiting from the variational
evidence lower bound, the facial images are encoded and disentangled into an
age-irrelevant distribution and an age-related distribution in the latent
space. A conditional introspective adversarial learning mechanism is introduced
to boost the image quality. In this way, when manipulating the age-related
distribution, UVA can achieve age translation with arbitrary ages. Further, by
sampling noise from the age-irrelevant distribution, we can generate
photorealistic facial images with a specific age. Moreover, given an input face
image, the mean value of age-related distribution can be treated as an age
estimator. These indicate that UVA can efficiently and accurately estimate the
age-related distribution by a disentangling manner, even if the training
dataset performs a long-tailed age distribution. UVA is the first attempt to
achieve facial age analysis tasks, including age translation, age generation
and age estimation, in a universal framework. The qualitative and quantitative
experiments demonstrate the superiority of UVA on five popular datasets,
including CACD2000, Morph, UTKFace, MegaAge-Asian and FG-NET.
