  We propose a novel deep generative model based on causal convolutions for
multi-subject motion modeling and synthesis, which is inspired by the success
of WaveNet in multi-subject speech synthesis. However, it is nontrivial to
adapt WaveNet to handle high-dimensional and physically constrained motion
data. To this end, we add an encoder and a decoder to the WaveNet to translate
the motion data into features and back to the predicted motions. We also add 1D
convolution layers to take skeleton configuration as an input to model skeleton
variations across different subjects. As a result, our network can scale up
well to large-scale motion data sets across multiple subjects and support
various applications, such as random and controllable motion synthesis, motion
denoising, and motion completion, in a unified way. Complex motions, such as
punching, kicking and, kicking while punching, are also well handled. Moreover,
our network can synthesize motions for novel skeletons not in the training
dataset. After fine-tuning the network with a few motion data of the novel
skeleton, it is able to capture the personalized style implied in the motion
and generate high-quality motions for the skeleton. Thus, it has the potential
to be used as a pre-trained network in few-shot learning for motion modeling
and synthesis. Experimental results show that our model can effectively handle
the variation of skeleton configurations, and it runs fast to synthesize
different types of motions on-line. We also perform user studies to verify that
the quality of motions generated by our network is superior to the motions of
state-of-the-art human motion synthesis methods.
