  With the advent of an increasing number of Augmented and Virtual Reality
applications that aim to perform meaningful and controlled style edits on
images of human faces, the impetus for the task of parsing face images to
produce accurate and fine-grained semantic segmentation maps is more than ever
before. Few State of the Art (SOTA) methods which solve this problem, do so by
incorporating priors with respect to facial structure or other face attributes
such as expression and pose in their deep classifier architecture. Our
endeavour in this work is to do away with the priors and complex pre-processing
operations required by SOTA multi-class face segmentation models by reframing
this operation as a downstream task post infusion of disentanglement with
respect to facial semantic regions of interest (ROIs) in the latent space of a
Generative Autoencoder model. We present results for our model's performance on
the CelebAMask-HQ and HELEN datasets. The encoded latent space of our model
achieves significantly higher disentanglement with respect to semantic ROIs
than that of other SOTA works. Moreover, it achieves a 13% faster inference
rate and comparable accuracy with respect to the publicly available SOTA for
the downstream task of semantic segmentation of face images.
