Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Video-Based Performance Cloning   We present a new video-based performance cloning technique. After training a
deep generative network using a reference video capturing the appearance and
dynamics of a target actor, we are able to generate videos where this actor
reenacts other performances. All of the training data and the driving
performances are provided as ordinary video segments, without motion capture or
depth information. Our generative model is realized as a deep neural network
with two branches, both of which train the same space-time conditional
generator, using shared weights. One branch, responsible for learning to
generate the appearance of the target actor in various poses, uses
\emph{paired} training data, self-generated from the reference video. The
second branch uses unpaired data to improve generation of temporally coherent
video renditions of unseen pose sequences. We demonstrate a variety of
promising results, where our method is able to generate temporally coherent
videos, for challenging scenarios where the reference and driving videos
consist of very different dance performances. Supplementary video:
https://youtu.be/JpwsEeqNhhA.

23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph