FExGAN-Meta: Facial Expression Generation with Meta Humans
TL
TL;DR
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.
Abstract
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.
References15 items
1
Explore the Expression: Facial Expression Generation using Auxiliary Classifier Generative Adversarial Network
2
Generative Adversarial Networks
3
Facial Emotion Recognition: State of the Art Performance on FER2013
4
Emotiongan: Facial Expression Synthesis Based on Pre-Trained Generator
5