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.
Cite
@article{arxiv.1908.02671,
title = {Dual-reference Age Synthesis},
author = {Yuan Zhou and Bingzhang Hu and and Jun He and Yu Guan and Ling Shao},
journal= {arXiv preprint arXiv:1908.02671},
year = {2020}
}