Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Video Content Swapping Using GAN   Video generation is an interesting problem in computer vision. It is quite
popular for data augmentation, special effect in move, AR/VR and so on. With
the advances of deep learning, many deep generative models have been proposed
to solve this task. These deep generative models provide away to utilize all
the unlabeled images and videos online, since it can learn deep feature
representations with unsupervised manner. These models can also generate
different kinds of images, which have great value for visual application.
However generating a video would be much more challenging since we need to
model not only the appearances of objects in the video but also their temporal
motion. In this work, we will break down any frame in the video into content
and pose. We first extract the pose information from a video using a
pre-trained human pose detection and use a generative model to synthesize the
video based on the content code and pose code.

30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
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The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
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The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
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The categories of this paper are: eess