  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). 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 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 acRNN
is able to accomplish this by explicitly accommodating for autoregressive noise
accumulation during training. 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.
