Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild
  Environment   We present an approach to estimate 3D poses of multiple people from multiple
camera views. In contrast to the previous efforts which require to establish
cross-view correspondence based on noisy and incomplete 2D pose estimations, we
present an end-to-end solution which directly operates in the $3$D space,
therefore avoids making incorrect decisions in the 2D space. To achieve this
goal, the features in all camera views are warped and aggregated in a common 3D
space, and fed into Cuboid Proposal Network (CPN) to coarsely localize all
people. Then we propose Pose Regression Network (PRN) to estimate a detailed 3D
pose for each proposal. The approach is robust to occlusion which occurs
frequently in practice. Without bells and whistles, it outperforms the
state-of-the-arts on the public datasets. Code will be released at
https://github.com/microsoft/multiperson-pose-estimation-pytorch.

28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th