Diverse and Lifespan Facial Age Transformation Synthesis with Identity Variation Rationality Metric

TL;DR

This paper introduces ${ m{DLAT}}^{oldsymbol{+}}$, a novel method for generating diverse, realistic facial age transformations while measuring and ensuring identity consistency across ages, addressing longstanding challenges in face aging synthesis.

Contribution

The paper presents a new model that produces diverse facial aging effects and a metric to evaluate identity rationality, improving realism and consistency in age progression and regression.

Findings

Effective synthesis of diverse age-progressed faces

New metric accurately measures identity deviation across ages

Model maintains identity consistency while enhancing diversity

Abstract

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 ${\rm{DLAT}}^{\boldsymbol{+}}$ to 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.