High Resolution Face Age Editing

• URL: http://arxiv.org/abs/2005.04410v1
• Date: Sat, 9 May 2020 09:59:51 GMT
• Title: High Resolution Face Age Editing
• Authors: Xu Yao, Gilles Puy, Alasdair Newson, Yann Gousseau, Pierre Hellier
• Abstract summary: adversarial training has produced some of the most visually impressive results for image manipulation. We present an encoder-decoder architecture for face age editing.
• Score: 5.809784853115826
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Face age editing has become a crucial task in film post-production, and is also becoming popular for general purpose photography. Recently, adversarial training has produced some of the most visually impressive results for image manipulation, including the face aging/de-aging task. In spite of considerable progress, current methods often present visual artifacts and can only deal with low-resolution images. In order to achieve aging/de-aging with the high quality and robustness necessary for wider use, these problems need to be addressed. This is the goal of the present work. We present an encoder-decoder architecture for face age editing. The core idea of our network is to create both a latent space containing the face identity, and a feature modulation layer corresponding to the age of the individual. We then combine these two elements to produce an output image of the person with a desired target age. Our architecture is greatly simplified with respect to other approaches, and allows for continuous age editing on high resolution images in a single unified model.

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