Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Temporally Coherent Person Matting Trained on Fake-Motion Dataset   We propose a novel neural-network-based method to perform matting of videos
depicting people that does not require additional user input such as trimaps.
Our architecture achieves temporal stability of the resulting alpha mattes by
using motion-estimation-based smoothing of image-segmentation algorithm
outputs, combined with convolutional-LSTM modules on U-Net skip connections.
  We also propose a fake-motion algorithm that generates training clips for the
video-matting network given photos with ground-truth alpha mattes and
background videos. We apply random motion to photos and their mattes to
simulate movement one would find in real videos and composite the result with
the background clips. It lets us train a deep neural network operating on
videos in an absence of a large annotated video dataset and provides
ground-truth training-clip foreground optical flow for use in loss functions.

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The categories of this paper are: quant-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: solv-int
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The categories of this paper are: supr-con
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The categories of this paper are: econ
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The categories of this paper are: nucl-ex
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The categories of this paper are: bayes-an
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The categories of this paper are: adap-org
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The categories of this paper are: alg-geom
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The categories of this paper are: funct-an
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The categories of this paper are: astro-ph
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The categories of this paper are: math-ph
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The categories of this paper are: q-bio
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The categories of this paper are: nucl-th
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-ex