Diverse and Lifespan Facial Age Transformation Synthesis with Identity
  Variation Rationality Metric
Jiu-Cheng Xie Jun Yang Wenqing Wang Feng Xu Hao Gao
Face aging has received continuous research attention over the past two
decades. Although previous works on this topic have achieved impressive
success, two longstanding problems remain unsettled: 1) generating diverse and
plausible facial aging patterns at the target age stage; 2) measuring the
rationality of identity variation between the original portrait and its
syntheses with age progression or regression. In this paper, we introduce DLAT
+ , the first algorithm that can realize Diverse and Lifespan Age
Transformation on human faces, where the diversity jointly manifests in the
transformation of facial textures and shapes. Apart from the diversity
mechanism embedded in the model, multiple consistency restrictions are
leveraged to keep it away from counterfactual aging syntheses. Moreover, we
propose a new metric to assess the rationality of Identity Deviation under Age
Gaps (IDAG) between the input face and its series of age-transformed
generations, which is based on statistical laws summarized from plenty of
genuine face-aging data. Extensive experimental results demonstrate the
uniqueness and effectiveness of our method in synthesizing diverse and
perceptually reasonable faces across the whole lifetime.