  Motion capture is facing some new possibilities brought by the inertial
sensing technologies which do not suffer from occlusion or wide-range
recordings as vision-based solutions do. However, as the recorded signals are
sparse and quite noisy, online performance and global translation estimation
turn out to be two key difficulties. In this paper, we present TransPose, a
DNN-based approach to perform full motion capture (with both global
translations and body poses) from only 6 Inertial Measurement Units (IMUs) at
over 90 fps. For body pose estimation, we propose a multi-stage network that
estimates leaf-to-full joint positions as intermediate results. This design
makes the pose estimation much easier, and thus achieves both better accuracy
and lower computation cost. For global translation estimation, we propose a
supporting-foot-based method and an RNN-based method to robustly solve for the
global translations with a confidence-based fusion technique. Quantitative and
qualitative comparisons show that our method outperforms the state-of-the-art
learning- and optimization-based methods with a large margin in both accuracy
and efficiency. As a purely inertial sensor-based approach, our method is not
limited by environmental settings (e.g., fixed cameras), making the capture
free from common difficulties such as wide-range motion space and strong
occlusion.
