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We present a real-time method for synthesizing highly complex human motions using a novel training regime we call the auto-conditioned Recurrent Neural Network (acRNN).
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Graham W Taylor, Geoffrey E Hinton, and Sam T Roweis · 2007
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Gaussian process dynamical models for human motion
Jack M Wang, David J Fleet, and Aaron Hertzmann · 2008
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Yongjoon Lee, Kevin Wampler, Gilbert Bernstein, Jovan Popović, and Zoran Popović · 2010
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Ilya Sutskever, James Martens, and Geoffrey E Hinton · 2011
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Jochen Tautges, Arno Zinke, Björn Krüger, Jan Baumann, Andreas Weber, Thomas Helten, Meinard Müller, Hans-Peter Seidel, and Bernd Eberhardt · 2011
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Thomas Geijtenbeek and Nicolas Pronost · 2012
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Sergey Levine, Jack M Wang, Alexis Haraux, Zoran Popović, and Vladlen Koltun · 2012
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Perttu Hämäläinen, Joose Rajamäki, and C Karen Liu · 2015
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Daniel Holden, Jun Saito, Taku Komura, and Thomas Joyce · 2015
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Andrej Karpathy · 2015
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Realtime style transfer for unlabeled heterogeneous human motion
Shihong Xia, Congyi Wang, Jinxiang Chai, and Jessica Hodgins · 2015
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Daniel Holden, Jun Saito, and Taku Komura · 2016
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Deep representation learning for human motion prediction and classification
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