Auto-Conditioned LSTM Network for Extended Complex Human Motion Synthesis

Anonymous

Nov 03, 2017 (modified: Nov 03, 2017) ICLR 2018 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: 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 motions 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 motions, such as walking or running. In contrast, our approach can synthesize arbitrary motions with highly complex styles, including dances or martial arts in addition to locomotion. The acLSTM is able to accomplish this by explicitly accommodating for autoregressive noise accumulation during training. Furthermore, the structure of the acLSTM is modular and compatible with any other recurrent network architecture, and is usable for tasks other than motion. Our work is the first to our knowledge that demonstrates the ability to generate over 18,000 continuous frames (300 seconds) of new complex human motion w.r.t. different styles.
  • TL;DR: Synthesize complex and extended human motions using an auto-conditioned LSTM network
  • Keywords: motion synthesis, motion prediction, human pose, human motion, recurrent networks, lstm

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