  We present a method for simultaneously estimating 3D human pose and body
shape from a sparse set of wide-baseline camera views. We train a symmetric
convolutional autoencoder with a dual loss that enforces learning of a latent
representation that encodes skeletal joint positions, and at the same time
learns a deep representation of volumetric body shape. We harness the latter to
up-scale input volumetric data by a factor of $4 \times$, whilst recovering a
3D estimate of joint positions with equal or greater accuracy than the state of
the art. Inference runs in real-time (25 fps) and has the potential for passive
human behaviour monitoring where there is a requirement for high fidelity
estimation of human body shape and pose.
