  We introduce MotioNet, a deep neural network that directly reconstructs the
motion of a 3D human skeleton from monocular video.While previous methods rely
on either rigging or inverse kinematics (IK) to associate a consistent skeleton
with temporally coherent joint rotations, our method is the first data-driven
approach that directly outputs a kinematic skeleton, which is a complete,
commonly used, motion representation. At the crux of our approach lies a deep
neural network with embedded kinematic priors, which decomposes sequences of 2D
joint positions into two separate attributes: a single, symmetric, skeleton,
encoded by bone lengths, and a sequence of 3D joint rotations associated with
global root positions and foot contact labels. These attributes are fed into an
integrated forward kinematics (FK) layer that outputs 3D positions, which are
compared to a ground truth. In addition, an adversarial loss is applied to the
velocities of the recovered rotations, to ensure that they lie on the manifold
of natural joint rotations. The key advantage of our approach is that it learns
to infer natural joint rotations directly from the training data, rather than
assuming an underlying model, or inferring them from joint positions using a
data-agnostic IK solver. We show that enforcing a single consistent skeleton
along with temporally coherent joint rotations constrains the solution space,
leading to a more robust handling of self-occlusions and depth ambiguities.
