Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Human Pose Models from Synthesized Data for Robust RGB-D Action
  Recognition   We propose Human Pose Models that represent RGB and depth images of human
poses independent of clothing textures, backgrounds, lighting conditions, body
shapes and camera viewpoints. Learning such universal models requires training
images where all factors are varied for every human pose. Capturing such data
is prohibitively expensive. Therefore, we develop a framework for synthesizing
the training data. First, we learn representative human poses from a large
corpus of real motion captured human skeleton data. Next, we fit synthetic 3D
humans with different body shapes to each pose and render each from 180 camera
viewpoints while randomly varying the clothing textures, background and
lighting. Generative Adversarial Networks are employed to minimize the gap
between synthetic and real image distributions. CNN models are then learned
that transfer human poses to a shared high-level invariant space. The learned
CNN models are then used as invariant feature extractors from real RGB and
depth frames of human action videos and the temporal variations are modelled by
Fourier Temporal Pyramid. Finally, linear SVM is used for classification.
Experiments on three benchmark cross-view human action datasets show that our
algorithm outperforms existing methods by significant margins for RGB only and
RGB-D action recognition.

20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
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The categories of this paper are: dg-ga
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The categories of this paper are: alg-geom
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The categories of this paper are: chem-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
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The categories of this paper are: plasm-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: econ
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: math-ph
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The categories of this paper are: adap-org
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The categories of this paper are: stat