  We propose a method to disentangle linear-encoded facial semantics from
StyleGAN without external supervision. The method derives from linear
regression and sparse representation learning concepts to make the disentangled
latent representations easily interpreted as well. We start by coupling
StyleGAN with a stabilized 3D deformable facial reconstruction method to
decompose single-view GAN generations into multiple semantics. Latent
representations are then extracted to capture interpretable facial semantics.
In this work, we make it possible to get rid of labels for disentangling
meaningful facial semantics. Also, we demonstrate that the guided extrapolation
along the disentangled representations can help with data augmentation, which
sheds light on handling unbalanced data. Finally, we provide an analysis of our
learned localized facial representations and illustrate that the semantic
information is encoded, which surprisingly complies with human intuition. The
overall unsupervised design brings more flexibility to representation learning
in the wild.
