Explore the Expression: Facial Expression Generation using Auxiliary Classifier Generative Adversarial Network
This work addresses the generation of facial expressions for applications in communication and AI, but it appears incremental as it builds on existing generative models.
The authors tackled the problem of generating diverse facial expressions for multiple character identities using a generative model, achieving robust generation and the ability to combine simple expressions into complex ones.
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.