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Biphasic facial age translation aims at predicting the appearance of theinput face at any age. Facial age translation has received considerableresearch attention in the last decade due to its practical value in cross-ageface recognition and various entertainment applications. However, most existingmethods model age changes between holistic images, regardless of the human facestructure and the age-changing patterns of individual facial components.Consequently, the lack of semantic supervision will cause infidelity ofgenerated faces in detail. To this end, we propose a unified framework forbiphasic facial age translation with noisy-semantic guided generativeadversarial networks. Structurally, we project the class-aware noisy semanticlayouts to soft latent maps for the following injection operation on theindividual facial parts. In particular, we introduce two sub-networks,ProjectionNet and ConstraintNet. ProjectionNet introduces the low-levelstructural semantic information with noise map and produces soft latent maps.ConstraintNet disentangles the high-level spatial features to constrain thesoft latent maps, which endows more age-related context into the soft latentmaps. Specifically, attention mechanism is employed in ConstraintNet forfeature disentanglement. Meanwhile, in order to mine the strongest mappingability of the network, we embed two types of learning strategies in thetraining procedure, supervised self-driven generation and unsupervisedcondition-driven cycle-consistent generation. As a result, extensiveexperiments conducted on MORPH and CACD datasets demonstrate the prominentability of our proposed method which achieves state-of-the-art performance.

A Unified Framework for Biphasic Facial Age Translation with Noisy-Semantic Guided Generative Adversarial Networks