  Age synthesis methods typically take a single image as input and use a
specific number to control the age of the generated image. In this paper, we
propose a novel framework taking two images as inputs, named dual-reference age
synthesis (DRAS), which approaches the task differently; instead of using
"hard" age information, i.e. a fixed number, our model determines the target
age in a "soft" way, by employing a second reference image. Specifically, the
proposed framework consists of an identity agent, an age agent and a generative
adversarial network. It takes two images as input - an identity reference and
an age reference - and outputs a new image that shares corresponding features
with each. Experimental results on two benchmark datasets (UTKFace and CACD)
demonstrate the appealing performance and flexibility of the proposed
framework.
