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Dual reference age synthesis
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DOI：10.1016/j.neucom.2020.06.023.png)
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Abstract
En 中文
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. (c) 2020 Elsevier B.V. All rights reserved.
Keywords:
Age synthesis
Dual reference
''Soft age information
Conditional generative adversarial network
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