  Facial expressions are a form of non-verbal communication that humans perform
seamlessly for meaningful transfer of information. Most of the literature
addresses the facial expression recognition aspect however, with the advent of
Generative Models, it has become possible to explore the affect space in
addition to mere classification of a set of expressions. In this article, we
propose a generative model architecture which robustly generates a set of
facial expressions for multiple character identities and explores the
possibilities of generating complex expressions by combining the simple ones.
