  We present a novel generative model for human motion modeling using
Generative Adversarial Networks (GANs). We formulate the GAN discriminator
using dense validation at each time-scale and perturb the discriminator input
to make it translation invariant. Our model is capable of motion generation and
completion. We show through our evaluations the resiliency to noise,
generalization over actions, and generation of long diverse sequences. We
evaluate our approach on Human 3.6M and CMU motion capture datasets using
inception scores.
