Motion capture from sparse inertial sensors has shown great potential
compared to image-based approaches since occlusions do not lead to a reduced
tracking quality and the recording space is not restricted to be within the
viewing frustum of the camera. However, capturing the motion and global
position only from a sparse set of inertial sensors is inherently ambiguous and
challenging. In consequence, recent state-of-the-art methods can barely handle
very long period motions, and unrealistic artifacts are common due to the
unawareness of physical constraints. To this end, we present the first method
which combines a neural kinematics estimator and a physics-aware motion
optimizer to track body motions with only 6 inertial sensors. The kinematics
module first regresses the motion status as a reference, and then the physics
module refines the motion to satisfy the physical constraints. Experiments
demonstrate a clear improvement over the state of the art in terms of capture
accuracy, temporal stability, and physical correctness.

利用 6 个惯性传感器，通过神经运动学估计器和物理感知运动优化器，实现身体运动的跟踪和重建，并在运动捕获精度、时间稳定性和物理正确性方面实现了极大的提高。