  Studies on the automatic processing of 3D human pose data have flourished in
the recent past. In this paper, we are interested in the generation of
plausible and diverse future human poses following an observed 3D pose
sequence. Current methods address this problem by injecting random variables
from a single latent space into a deterministic motion prediction framework,
which precludes the inherent multi-modality in human motion generation. In
addition, previous works rarely explore the use of attention to select which
frames are to be used to inform the generation process up to our knowledge. To
overcome these limitations, we propose Hierarchical Transformer Dynamical
Variational Autoencoder, HiT-DVAE, which implements auto-regressive generation
with transformer-like attention mechanisms. HiT-DVAE simultaneously learns the
evolution of data and latent space distribution with time correlated
probabilistic dependencies, thus enabling the generative model to learn a more
complex and time-varying latent space as well as diverse and realistic human
motions. Furthermore, the auto-regressive generation brings more flexibility on
observation and prediction, i.e. one can have any length of observation and
predict arbitrary large sequences of poses with a single pre-trained model. We
evaluate the proposed method on HumanEva-I and Human3.6M with various
evaluation methods, and outperform the state-of-the-art methods on most of the
metrics.
