Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Neural Network Approach to Missing Marker Reconstruction in Human
  Motion Capture   Optical motion capture systems have become a widely used technology in
various fields, such as augmented reality, robotics, movie production, etc.
Such systems use a large number of cameras to triangulate the position of
optical markers.The marker positions are estimated with high accuracy. However,
especially when tracking articulated bodies, a fraction of the markers in each
timestep is missing from the reconstruction. In this paper, we propose to use a
neural network approach to learn how human motion is temporally and spatially
correlated, and reconstruct missing markers positions through this model. We
experiment with two different models, one LSTM-based and one time-window-based.
Both methods produce state-of-the-art results, while working online, as opposed
to most of the alternative methods, which require the complete sequence to be
known. The implementation is publicly available at
https://github.com/Svito-zar/NN-for-Missing-Marker-Reconstruction .

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The categories of this paper are: physics
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
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The categories of this paper are: patt-sol
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The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-th