Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DeepMovie: Using Optical Flow and Deep Neural Networks to Stylize Movies   A recent paper by Gatys et al. describes a method for rendering an image in
the style of another image. First, they use convolutional neural network
features to build a statistical model for the style of an image. Then they
create a new image with the content of one image but the style statistics of
another image. Here, we extend this method to render a movie in a given
artistic style. The naive solution that independently renders each frame
produces poor results because the features of the style move substantially from
one frame to the next. The other naive method that initializes the optimization
for the next frame using the rendered version of the previous frame also
produces poor results because the features of the texture stay fixed relative
to the frame of the movie instead of moving with objects in the scene. The main
contribution of this paper is to use optical flow to initialize the style
transfer optimization so that the texture features move with the objects in the
video. Finally, we suggest a method to incorporate optical flow explicitly into
the cost function.

5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph