Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A review of 3D human pose estimation algorithms for markerless motion
  capture   Human pose estimation is a very active research field, stimulated by its
important applications in robotics, entertainment or health and sports
sciences, among others. Advances in convolutional networks triggered noticeable
improvements in 2D pose estimation, leading modern 3D markerless motion capture
techniques to an average error per joint of 20 mm. However, with the
proliferation of methods, it is becoming increasingly difficult to make an
informed choice. Here, we review the leading human pose estimation methods of
the past five years, focusing on metrics, benchmarks and method structures. We
propose a taxonomy based on accuracy, speed and robustness that we use to
classify de methods and derive directions for future research.

32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
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The categories of this paper are: nlin
3
The categories of this paper are: physics
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The categories of this paper are: stat
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The categories of this paper are: patt-sol
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The categories of this paper are: plasm-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: econ
23
The categories of this paper are: solv-int