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Age progression and regression aim to synthesize photorealistic appearance ofa given face image with aging and rejuvenation effects, respectively. Existinggenerative adversarial networks (GANs) based methods suffer from the followingthree major issues: 1) unstable training introducing strong ghost artifacts inthe generated faces, 2) unpaired training leading to unexpected changes infacial attributes such as genders and races, and 3) non-bijective age mappingsincreasing the uncertainty in the face transformation. To overcome theseissues, this paper proposes a novel framework, termed AgeFlow, to integrate theadvantages of both flow-based models and GANs. The proposed AgeFlow containsthree parts: an encoder that maps a given face to a latent space through aninvertible neural network, a novel invertible conditional translation module(ICTM) that translates the source latent vector to target one, and a decoderthat reconstructs the generated face from the target latent vector using thesame encoder network; all parts are invertible achieving bijective agemappings. The novelties of ICTM are two-fold. First, we propose anattribute-aware knowledge distillation to learn the manipulation direction ofage progression while keeping other unrelated attributes unchanged, alleviatingunexpected changes in facial attributes. Second, we propose to use GANs in thelatent space to ensure the learned latent vector indistinguishable from thereal ones, which is much easier than traditional use of GANs in the imagedomain. Experimental results demonstrate superior performance over existingGANs-based methods on two benchmarked datasets. The source code is available athttps://github.com/Hzzone/AgeFlow.

AgeFlow: Conditional Age Progression and Regression with Normalizing Flows