FExGAN-Meta: Facial Expression Generation with Meta Humans
This addresses facial expression generation for Meta-Humans, which is an incremental domain-specific application.
The paper tackles the challenge of generating and classifying facial expressions for Meta-Humans by proposing FExGAN-Meta, which robustly handles both simple and complex expressions on a newly collected dataset of ten Meta-Humans in a studio environment.
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.