Fusion Poser: 3D Human Pose Estimation Using Sparse IMUs and Head Trackers in Real Time

The motion capture method using sparse inertial sensors is an approach for solving the occlusion and economic problems in vision-based methods, which is suitable for virtual reality applications and works in complex environments. However, VR applications need to track the location of the user in real-world space, which is hard to obtain using only inertial sensors. In this paper, we present Fusion Poser, which combines the deep learning-based pose estimation and location tracking method with six inertial measurement units and a head tracking sensor that provides head-mounted displays. To estimate human poses, we propose a bidirectional recurrent neural network with a convolutional long short-term memory layer that achieves higher accuracy and stability by preserving spatio-temporal properties. To locate a user with real-world coordinates, our method integrates the results of an estimated joint pose with the pose of the tracker. To train the model, we gathered public motion capture datasets of synthesized IMU measurement data, as well as creating a real-world dataset. In the evaluation, our method showed higher accuracy and a more robust estimation performance, especially when the user adopted lower poses, such as a squat or a bow.