Finally, weĭemonstrate that the end-to-end nature of our approach, coupled with the rich semantic latent space The path controlling age, our method learns a more disentangled, non-linear path. Moreover, unlike other approaches that operate solely in the latent space using a prior on In this formulation, our method approaches the continuous aging process as a regression taskīetween the input age and desired target age, providing fine-grained control over the generated Used to explicitly guide the encoder in generating the latent codes corresponding to the desiredĪge. We employ a pre-trained age regression network GAN (e.g., StyleGAN) subject to a given aging shift. That learns to directly encode real facial images into the latent space of a pre-trained unconditional In this work, we present an image-to-image translation method Accurately modeling this complex transformation over an input facial image is extremely challengingĪs it requires making convincing and possibly large changes to facial features and head shape, while The task of age transformation illustrates the change of an individual's appearance over time.
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