  With the popularization of game and VR/AR devices, there is a growing need
for capturing human motion with a sparse set of tracking data. In this paper,
we introduce a deep neural-network (DNN) based method for real-time prediction
of the lower-body pose only from the tracking signals of the upper-body joints.
Specifically, our Gated Recurrent Unit (GRU)-based recurrent architecture
predicts the lower-body pose and feet contact probability from past sequence of
tracking signals of the head, hands and pelvis. A major feature of our method
is that the input signal is represented with the velocity of tracking signals.
We show that the velocity representation better models the correlation between
the upper-body and lower-body motions and increase the robustness against the
diverse scales and proportions of the user body than position-orientation
representations. In addition, to remove foot-skating and floating artifacts,
our network predicts feet contact state, which is used to post-process the
lower-body pose with inverse kinematics to preserve the contact. Our network is
lightweight so as to run in real-time applications. We show the effectiveness
of our method through several quantitative evaluations against other
architectures and input representations, with respect to wild tracking data
obtained from commercial VR devices.
