FExGAN-Meta: Facial Expression Generation with Meta Humans

Published in

arXiv.org(2022)

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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.

Authors

J. R. Siddiqui

2 papers

References15 items

Explore the Expression: Facial Expression Generation using Auxiliary Classifier Generative Adversarial Network

Generative Adversarial Networks

Facial Emotion Recognition: State of the Art Performance on FER2013

Emotiongan: Facial Expression Synthesis Based on Pre-Trained Generator

Facial Expression Recognition Using Disentangled Adversarial Learning

FExGAN-Meta: Facial Expression Generation with Meta Humans

Published in

arXiv.org(2022)

External Links:

Generate Graph DownloadPDF

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

Authors

J. R. Siddiqui

2 papers

FExGAN-Meta: Facial Expression Generation with Meta Humans

TL;DR

Abstract

Authors

References15 items

Explore the Expression: Facial Expression Generation using Auxiliary Classifier Generative Adversarial Network

Generative Adversarial Networks

Facial Emotion Recognition: State of the Art Performance on FER2013

Emotiongan: Facial Expression Synthesis Based on Pre-Trained Generator

Facial Expression Recognition Using Disentangled Adversarial Learning

FExGAN-Meta: Facial Expression Generation with Meta Humans

TL;DR

Abstract

Authors

References15 items

Explore the Expression: Facial Expression Generation using Auxiliary Classifier Generative Adversarial Network

Generative Adversarial Networks

Facial Emotion Recognition: State of the Art Performance on FER2013

Emotiongan: Facial Expression Synthesis Based on Pre-Trained Generator

Facial Expression Recognition Using Disentangled Adversarial Learning

cGAN Based Facial Expression Recognition for Human-Robot Interaction

Deep Learning Models for Facial Expression Recognition

A Compact Deep Learning Model for Robust Facial Expression Recognition

VGGFace2: A Dataset for Recognising Faces across Pose and Age

Conditional Image Synthesis with Auxiliary Classifier GANs

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Conditional Generative Adversarial Nets

Metahuman creator

Deep Face Recognition

British Machine Vision Association

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