  Semantic face editing of real world facial images is an important application
of generative models. Recently, multiple works have explored possible
techniques to generate such modifications using the latent structure of
pre-trained GAN models. However, such approaches often require training an
encoder network and that is typically a time-consuming and resource intensive
process. A possible alternative to such a GAN-based architecture can be
styleALAE, a latent-space based autoencoder that can generate photo-realistic
images of high quality. Unfortunately, the reconstructed image in styleALAE
does not preserve the identity of the input facial image. This limits the
application of styleALAE for semantic face editing of images with known
identities. In our work, we use a recent advancement in one-shot domain
adaptation to address this problem. Our work ensures that the identity of the
reconstructed image is the same as the given input image. We further generate
semantic modifications over the reconstructed image by using the latent space
of the pre-trained styleALAE model. Results show that our approach can generate
semantic modifications on any real world facial image while preserving the
identity.

Computer Vision and Pattern Recognition