Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: FExGAN-Meta: Facial Expression Generation with Meta Humans   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.

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The categories of this paper are: chem-ph
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The categories of this paper are: hep-th
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: solv-int
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The categories of this paper are: funct-an
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The categories of this paper are: cond-mat
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The categories of this paper are: econ
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The categories of this paper are: cmp-lg
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The categories of this paper are: math-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-bio
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The categories of this paper are: alg-geom
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The categories of this paper are: stat
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The categories of this paper are: bayes-an
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The categories of this paper are: quant-ph