  Biphasic facial age translation aims at predicting the appearance of the
input face at any age. Facial age translation has received considerable
research attention in the last decade due to its practical value in cross-age
face recognition and various entertainment applications. However, most existing
methods model age changes between holistic images, regardless of the human face
structure and the age-changing patterns of individual facial components.
Consequently, the lack of semantic supervision will cause infidelity of
generated faces in detail. To this end, we propose a unified framework for
biphasic facial age translation with noisy-semantic guided generative
adversarial networks. Structurally, we project the class-aware noisy semantic
layouts to soft latent maps for the following injection operation on the
individual facial parts. In particular, we introduce two sub-networks,
ProjectionNet and ConstraintNet. ProjectionNet introduces the low-level
structural semantic information with noise map and produces soft latent maps.
ConstraintNet disentangles the high-level spatial features to constrain the
soft latent maps, which endows more age-related context into the soft latent
maps. Specifically, attention mechanism is employed in ConstraintNet for
feature disentanglement. Meanwhile, in order to mine the strongest mapping
ability of the network, we embed two types of learning strategies in the
training procedure, supervised self-driven generation and unsupervised
condition-driven cycle-consistent generation. As a result, extensive
experiments conducted on MORPH and CACD datasets demonstrate the prominent
ability of our proposed method which achieves state-of-the-art performance.
