Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Skeleton Transformer Networks: 3D Human Pose and Skinned Mesh from
  Single RGB Image   In this paper, we present Skeleton Transformer Networks (SkeletonNet), an
end-to-end framework that can predict not only 3D joint positions but also 3D
angular pose (bone rotations) of a human skeleton from a single color image.
This in turn allows us to generate skinned mesh animations. Here, we propose a
two-step regression approach. The first step regresses bone rotations in order
to obtain an initial solution by considering skeleton structure. The second
step performs refinement based on heatmap regressor using a 3D pose
representation called cross heatmap which stacks heatmaps of xy and zy
coordinates. By training the network using the proposed 3D human pose dataset
that is comprised of images annotated with 3D skeletal angular poses, we showed
that SkeletonNet can predict a full 3D human pose (joint positions and bone
rotations) from a single image in-the-wild.

21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int