  The subtleness of human facial expressions and a large degree of variation in
the level of intensity to which a human expresses them is what makes it
challenging to robustly classify and generate images of facial expressions.
Lack of good quality data can hinder the performance of a deep learning model.
In this article, we have proposed a Facial Expression Generation method for
Meta-Humans (FExGAN-Meta) that works robustly with the images of Meta-Humans.
We have prepared a large dataset of facial expressions exhibited by ten
Meta-Humans when placed in a studio environment and then we have evaluated
FExGAN-Meta on the collected images. The results show that FExGAN-Meta robustly
generates and classifies the images of Meta-Humans for the simple as well as
the complex facial expressions.
