  To represent people in mixed reality applications for collaboration and
communication, we need to generate realistic and faithful avatar poses.
However, the signal streams that can be applied for this task from head-mounted
devices (HMDs) are typically limited to head pose and hand pose estimates.
While these signals are valuable, they are an incomplete representation of the
human body, making it challenging to generate a faithful full-body avatar. We
address this challenge by developing a flow-based generative model of the 3D
human body from sparse observations, wherein we learn not only a conditional
distribution of 3D human pose, but also a probabilistic mapping from
observations to the latent space from which we can generate a plausible pose
along with uncertainty estimates for the joints. We show that our approach is
not only a strong predictive model, but can also act as an efficient pose prior
in different optimization settings where a good initial latent code plays a
major role.
