  We propose a new representation of human body motion which encodes a full
motion in a sequence of latent motion primitives. Recently, task generic motion
priors have been introduced and propose a coherent representation of human
motion based on a single latent code, with encouraging results for many tasks.
Extending these methods to longer motion with various duration and framerate is
all but straightforward as one latent code proves inefficient to encode longer
term variability. Our hypothesis is that long motions are better represented as
a succession of actions than in a single block. By leveraging a
sequence-to-sequence architecture, we propose a model that simultaneously
learns a temporal segmentation of motion and a prior on the motion segments. To
provide flexibility with temporal resolution and motion duration, our
representation is continuous in time and can be queried for any timestamp. We
show experimentally that our method leads to a significant improvement over
state-of-the-art motion priors on a spatio-temporal completion task on sparse
pointclouds. Code will be made available upon publication.
