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Semantic face editing of real world facial images is an important applicationof generative models. Recently, multiple works have explored possibletechniques to generate such modifications using the latent structure ofpre-trained GAN models. However, such approaches often require training anencoder network and that is typically a time-consuming and resource intensiveprocess. A possible alternative to such a GAN-based architecture can bestyleALAE, a latent-space based autoencoder that can generate photo-realisticimages of high quality. Unfortunately, the reconstructed image in styleALAEdoes not preserve the identity of the input facial image. This limits theapplication of styleALAE for semantic face editing of images with knownidentities. In our work, we use a recent advancement in one-shot domainadaptation to address this problem. Our work ensures that the identity of thereconstructed image is the same as the given input image. We further generatesemantic modifications over the reconstructed image by using the latent spaceof the pre-trained styleALAE model. Results show that our approach can generatesemantic modifications on any real world facial image while preserving theidentity.

One-shot domain adaptation for semantic face editing of real world images using StyleALAE