FExGAN-Meta: Facial Expression Generation with Meta Humans
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 15 references that are in the map. First posted on arXiv 17 Feb 2022.
Builds on
7 of 15 references.
References · 7
- 2014Conditional Generative Adversarial Netscited by 2,162
- 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
- 2017VGGFace2: A dataset for recognising faces across pose and agecited by 914
- 2016Conditional Image Synthesis With Auxiliary Classifier GANscited by 565
- 2021Facial Emotion Recognition: State of the Art Performance on FER2013cited by 20
- 2019Facial Expression Recognition Using Disentangled Adversarial Learningcited by 3
- 2022Explore the Expression: Facial Expression Generation using Auxiliary Classifier Generative Adversarial Networkcited by 2
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