# High Resolution Face Age Editing Source: [High Resolution Face Age Editing]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:42.780Z Authors: Xu Yao, Gilles Puy, Alasdair Newson, Yann Gousseau, Pierre Hellier Published: 2020-05-09T09:59:51.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Face Restoration and Age Editing on Cross-age dataset](): Face Restoration and Age Editing; Cross-age dataset; PSNR: 21.57. ## Original sources and implementation [Original paper]() [PDF]()