  This paper presents a novel recurrent neural network-based method to
construct a latent motion manifold that can represent a wide range of human
motions in a long sequence. We introduce several new components to increase the
spatial and temporal coverage in motion space while retaining the details of
motion capture data. These include new regularization terms for the motion
manifold, combination of two complementary decoders for predicting joint
rotations and joint velocities, and the addition of the forward kinematics
layer to consider both joint rotation and position errors. In addition, we
propose a set of loss terms that improve the overall quality of the motion
manifold from various aspects, such as the capability of reconstructing not
only the motion but also the latent manifold vector, and the naturalness of the
motion through adversarial loss. These components contribute to creating
compact and versatile motion manifold that allows for creating new motions by
performing random sampling and algebraic operations, such as interpolation and
analogy, in the latent motion manifold.
