  We propose a framework to learn a structured latent space to represent 4D
human body motion, where each latent vector encodes a full motion of the whole
3D human shape. On one hand several data-driven skeletal animation models exist
proposing motion spaces of temporally dense motion signals, but based on
geometrically sparse kinematic representations. On the other hand many methods
exist to build shape spaces of dense 3D geometry, but for static frames. We
bring together both concepts, proposing a motion space that is dense both
temporally and geometrically. Once trained, our model generates a multi-frame
sequence of dense 3D meshes based on a single point in a low-dimensional latent
space. This latent space is built to be structured, such that similar motions
form clusters. It also embeds variations of duration in the latent vector,
allowing semantically close sequences that differ only by temporal unfolding to
share similar latent vectors. We demonstrate experimentally the structural
properties of our latent space, and show it can be used to generate plausible
interpolations between different actions. We also apply our model to 4D human
motion completion, showing its promising abilities to learn spatio-temporal
features of human motion.
