Auto-Conditioned LSTM Network for Extended Complex Human Motion Synthesis

@article{Li2017AutoConditionedLN,
  title={Auto-Conditioned LSTM Network for Extended Complex Human Motion Synthesis},
  author={Zimo Li and Yi Zhou and Shuangjiu Xiao and Chong He and Hao Li},
  journal={CoRR},
  year={2017},
  volume={abs/1707.05363}
}
We present a real-time method for synthesizing highly complex human motions using a novel LSTM network training regime we call the auto-conditioned LSTM (acLSTM). Recently, researchers have attempted to synthesize new motion by using autoregressive techniques, but existing methods tend to freeze or diverge after a couple of seconds due to an accumulation of errors that are fed back into the network. Furthermore, such methods have only been shown to be reliable for relatively simple human… CONTINUE READING
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