  Pose and motion priors are crucial for recovering realistic and accurate
human motion from noisy observations. Substantial progress has been made on
pose and shape estimation from images, and recent works showed impressive
results using priors to refine frame-wise predictions. However, a lot of motion
priors only model transitions between consecutive poses and are used in
time-consuming optimization procedures, which is problematic for many
applications requiring real-time motion capture. We introduce Motion-DVAE, a
motion prior to capture the short-term dependencies of human motion. As part of
the dynamical variational autoencoder (DVAE) models family, Motion-DVAE
combines the generative capability of VAE models and the temporal modeling of
recurrent architectures. Together with Motion-DVAE, we introduce an
unsupervised learned denoising method unifying regression- and
optimization-based approaches in a single framework for real-time 3D human pose
estimation. Experiments show that the proposed approach reaches competitive
performance with state-of-the-art methods while being much faster.
