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Motion capture is facing some new possibilities brought by the inertialsensing technologies which do not suffer from occlusion or wide-rangerecordings as vision-based solutions do. However, as the recorded signals aresparse and quite noisy, online performance and global translation estimationturn out to be two key difficulties. In this paper, we present TransPose, aDNN-based approach to perform full motion capture (with both globaltranslations and body poses) from only 6 Inertial Measurement Units (IMUs) atover 90 fps. For body pose estimation, we propose a multi-stage network thatestimates leaf-to-full joint positions as intermediate results. This designmakes the pose estimation much easier, and thus achieves both better accuracyand lower computation cost. For global translation estimation, we propose asupporting-foot-based method and an RNN-based method to robustly solve for theglobal translations with a confidence-based fusion technique. Quantitative andqualitative comparisons show that our method outperforms the state-of-the-artlearning- and optimization-based methods with a large margin in both accuracyand efficiency. As a purely inertial sensor-based approach, our method is notlimited by environmental settings (e.g., fixed cameras), making the capturefree from common difficulties such as wide-range motion space and strongocclusion.

TransPose: Real-time 3D Human Translation and Pose Estimation with Six Inertial Sensors