  We tackle the task of diverse 3D human motion prediction, that is,
forecasting multiple plausible future 3D poses given a sequence of observed 3D
poses. In this context, a popular approach consists of using a Conditional
Variational Autoencoder (CVAE). However, existing approaches that do so either
fail to capture the diversity in human motion, or generate diverse but
semantically implausible continuations of the observed motion. In this paper,
we address both of these problems by developing a new variational framework
that accounts for both diversity and context of the generated future motion. To
this end, and in contrast to existing approaches, we condition the sampling of
the latent variable that acts as source of diversity on the representation of
the past observation, thus encouraging it to carry relevant information. Our
experiments demonstrate that our approach yields motions not only of higher
quality while retaining diversity, but also that preserve the contextual
information contained in the observed 3D pose sequence.
