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Reinforcement learning has shown great promise for synthesizing realistic human behaviors by learning humanoid control policies from motion capture data.
Diverse trajectory forecasting with determinantal point processes
Y. Yuan and K. Kitani · 1907
Earlier work this paper cites.
Interactive control of avatars animated with human motion data
J. Lee, J. Chai, P. S. Reitsma, J. K. Hodgins, and N. S. Pollard · 2002
Earlier work this paper cites.
Motion synthesis and editing in low-dimensional spaces
H. J. Shin and J. Lee · 2006
Earlier work this paper cites.
Construction and optimal search of interpolated motion graphs
A. Safonova and J. K. Hodgins · 2007
Earlier work this paper cites.
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K. Yin, K. Loken, and M. Van de Panne · 2007
Earlier work this paper cites.
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U. Muico, Y. Lee, J. Popović, and Z. Popović · 2009
Earlier work this paper cites.
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E. Coumans · 2010
Earlier work this paper cites.
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Earlier work this paper cites.
Motion fields for interactive character locomotion
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Continuous character control with low-dimensional embeddings
S. Levine, J. M. Wang, A. Haraux, Z. Popović, and V. Koltun · 2012
Earlier work this paper cites.
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E. Todorov, T. Erez, and Y. Tassa · 2012
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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D. P. Kingma and M. Welling · 2013
Earlier work this paper cites.
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Earlier work this paper cites.
Empirical evaluation of gated recurrent neural networks on sequence modeling
J. Chung, C. Gulcehre, K. Cho, and Y. Bengio · 2014
Earlier work this paper cites.
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