10/25/2023 0 Comments Age progression transformationFixed age classes are used as anchors to approximate continuous age transformation. The network is trained on the FFHQ dataset, which we labeled for ages, gender, and semantic segmentation. We propose a novel multi-domain image-to-image generative adversarial network architecture, whose learned latent space models a continuous bi-directional aging process. This limits the applicability of previous methods to aging of adults to slightly older adults, and application of those methods to photos of children does not produce quality results. Most existing aging methods are limited to changing the texture, overlooking transformations in head shape that occur during the human aging and growth process. We address the problem of single photo age progression and regression-the prediction of how a person might look in the future, or how they looked in the past.
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