  Data-driven character animation techniques rely on the existence of a
properly established model of motion, capable of describing its rich context.
However, commonly used motion representations often fail to accurately encode
the full articulation of motion, or present artifacts. In this work, we address
the fundamental problem of finding a robust pose representation for motion
modeling, suitable for deep character animation, one that can better constrain
poses and faithfully capture nuances correlated with skeletal characteristics.
Our representation is based on dual quaternions, the mathematical abstractions
with well-defined operations, which simultaneously encode rotational and
positional orientation, enabling a hierarchy-aware encoding, centered around
the root. We demonstrate that our representation overcomes common motion
artifacts, and assess its performance compared to other popular
representations. We conduct an ablation study to evaluate the impact of various
losses that can be incorporated during learning. Leveraging the fact that our
representation implicitly encodes skeletal motion attributes, we train a
network on a dataset comprising of skeletons with different proportions,
without the need to retarget them first to a universal skeleton, which causes
subtle motion elements to be missed. We show that smooth and natural poses can
be achieved, paving the way for fascinating applications.
