  Deep generative models have recently presented impressive results in
generating realistic face images of random synthetic identities.
  To generate multiple samples of a certain synthetic identity, previous works
proposed to disentangle the latent space of GANs by incorporating additional
supervision or regularization, enabling the manipulation of certain attributes.
Others proposed to disentangle specific factors in unconditional pretrained
GANs latent spaces to control their output, which also requires supervision by
attribute classifiers. Moreover, these attributes are entangled in GAN's latent
space, making it difficult to manipulate them without affecting the identity
information. We propose in this work a framework, ExFaceGAN, to disentangle
identity information in pretrained GANs latent spaces, enabling the generation
of multiple samples of any synthetic identity. Given a reference latent code of
any synthetic image and latent space of pretrained GAN, our ExFaceGAN learns an
identity directional boundary that disentangles the latent space into two
sub-spaces, with latent codes of samples that are either identity similar or
dissimilar to a reference image. By sampling from each side of the boundary,
our ExFaceGAN can generate multiple samples of synthetic identity without the
need for designing a dedicated architecture or supervision from attribute
classifiers. We demonstrate the generalizability and effectiveness of ExFaceGAN
by integrating it into learned latent spaces of three SOTA GAN approaches. As
an example of the practical benefit of our ExFaceGAN, we empirically prove that
data generated by ExFaceGAN can be successfully used to train face recognition
models (\url{https://github.com/fdbtrs/ExFaceGAN}).
