Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Video Autoencoder: self-supervised disentanglement of static 3D
  structure and motion   A video autoencoder is proposed for learning disentan- gled representations
of 3D structure and camera pose from videos in a self-supervised manner.
Relying on temporal continuity in videos, our work assumes that the 3D scene
structure in nearby video frames remains static. Given a sequence of video
frames as input, the video autoencoder extracts a disentangled representation
of the scene includ- ing: (i) a temporally-consistent deep voxel feature to
represent the 3D structure and (ii) a 3D trajectory of camera pose for each
frame. These two representations will then be re-entangled for rendering the
input video frames. This video autoencoder can be trained directly using a
pixel reconstruction loss, without any ground truth 3D or camera pose
annotations. The disentangled representation can be applied to a range of
tasks, including novel view synthesis, camera pose estimation, and video
generation by motion following. We evaluate our method on several large- scale
natural video datasets, and show generalization results on out-of-domain
images.

22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math