  A fundamental problem in computer animation is that of realizing purposeful
and realistic human movement given a sufficiently-rich set of motion capture
clips. We learn data-driven generative models of human movement using
autoregressive conditional variational autoencoders, or Motion VAEs. The latent
variables of the learned autoencoder define the action space for the movement
and thereby govern its evolution over time. Planning or control algorithms can
then use this action space to generate desired motions. In particular, we use
deep reinforcement learning to learn controllers that achieve goal-directed
movements. We demonstrate the effectiveness of the approach on multiple tasks.
We further evaluate system-design choices and describe the current limitations
of Motion VAEs.
