  In this paper, we have used Recurrent Neural Networks to capture and model
human motion data and generate motions by prediction of the next immediate data
point at each time-step. Our RNN is armed with recently proposed Gated
Recurrent Units which has shown promising results in some sequence modeling
problems such as Machine Translation and Speech Synthesis. We demonstrate that
this model is able to capture long-term dependencies in data and generate
realistic motions.
