Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Real-Time Human Motion Capture with Multiple Depth Cameras   Commonly used human motion capture systems require intrusive attachment of
markers that are visually tracked with multiple cameras. In this work we
present an efficient and inexpensive solution to markerless motion capture
using only a few Kinect sensors. Unlike the previous work on 3d pose estimation
using a single depth camera, we relax constraints on the camera location and do
not assume a co-operative user. We apply recent image segmentation techniques
to depth images and use curriculum learning to train our system on purely
synthetic data. Our method accurately localizes body parts without requiring an
explicit shape model. The body joint locations are then recovered by combining
evidence from multiple views in real-time. We also introduce a dataset of ~6
million synthetic depth frames for pose estimation from multiple cameras and
exceed state-of-the-art results on the Berkeley MHAD dataset.

11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con