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.

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This paper proposes a simple deformation method for editing the trajectory of a walking motion with preserving its style. To this end, our method analyzes the trajectory of the root joint into the graph and deforms it by applying the graph Laplace operator. The trajectory of the root joint is presented as a graph with a vertex defined the position and direction at each time frame on the motion dataThe graph transforms the trajectory into the differential coordinate, and if the constraints are set on the trajectory vertex, the solver iterative approaches to the solution. By modifying the root trajectory, we can continuously vary the walking motion, which reduces the cost of capturing a whole motion that is required. After computes the root trajectory, other joints are copied on the root and post-processed as a final motion. At the end of our paper, we show the application that the character continuously walks in a complex environment while satisfying user constraints.