Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Conditional Adversarial Synthesis of 3D Facial Action Units   Employing deep learning-based approaches for fine-grained facial expression
analysis, such as those involving the estimation of Action Unit (AU)
intensities, is difficult due to the lack of a large-scale dataset of real
faces with sufficiently diverse AU labels for training. In this paper, we
consider how AU-level facial image synthesis can be used to substantially
augment such a dataset. We propose an AU synthesis framework that combines the
well-known 3D Morphable Model (3DMM), which intrinsically disentangles
expression parameters from other face attributes, with models that
adversarially generate 3DMM expression parameters conditioned on given target
AU labels, in contrast to the more conventional approach of generating facial
images directly. In this way, we are able to synthesize new combinations of
expression parameters and facial images from desired AU labels. Extensive
quantitative and qualitative results on the benchmark DISFA dataset demonstrate
the effectiveness of our method on 3DMM facial expression parameter synthesis
and data augmentation for deep learning-based AU intensity estimation.

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The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
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The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
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The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
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The categories of this paper are: funct-an
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The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat