Fusing Monocular Images and Sparse IMU Signals for Real-time Human
  Motion Capture
Shaohua Pan Qi Ma Xinyu Yi Weifeng Hu Xiong Wang Xingkang Zhou Jijunnan Li Feng Xu
Either RGB images or inertial signals have been used for the task of motion
capture (mocap), but combining them together is a new and interesting topic. We
believe that the combination is complementary and able to solve the inherent
difficulties of using one modality input, including occlusions, extreme
lighting/texture, and out-of-view for visual mocap and global drifts for
inertial mocap. To this end, we propose a method that fuses monocular images
and sparse IMUs for real-time human motion capture. Our method contains a dual
coordinate strategy to fully explore the IMU signals with different goals in
motion capture. To be specific, besides one branch transforming the IMU signals
to the camera coordinate system to combine with the image information, there is
another branch to learn from the IMU signals in the body root coordinate system
to better estimate body poses. Furthermore, a hidden state feedback mechanism
is proposed for both two branches to compensate for their own drawbacks in
extreme input cases. Thus our method can easily switch between the two kinds of
signals or combine them in different cases to achieve a robust mocap. %The two
divided parts can help each other for better mocap results under different
conditions. Quantitative and qualitative results demonstrate that by delicately
designing the fusion method, our technique significantly outperforms the
state-of-the-art vision, IMU, and combined methods on both global orientation
and local pose estimation. Our codes are available for research at
https://shaohua-pan.github.io/robustcap-page/.