  Although many studies have investigated markerless motion capture, the
technology has not been applied to real sports or concerts. In this paper, we
propose a markerless motion capture method with spatiotemporal accuracy and
smoothness from multiple cameras in wide-space and multi-person environments.
The proposed method predicts each person's 3D pose and determines the bounding
box of multi-camera images small enough. This prediction and spatiotemporal
filtering based on human skeletal model enables 3D reconstruction of the person
and demonstrates high-accuracy. The accurate 3D reconstruction is then used to
predict the bounding box of each camera image in the next frame. This is
feedback from the 3D motion to 2D pose, and provides a synergetic effect on the
overall performance of video motion capture. We evaluated the proposed method
using various datasets and a real sports field. The experimental results
demonstrate that the mean per joint position error (MPJPE) is 31.5 mm and the
percentage of correct parts (PCP) is 99.5% for five people dynamically moving
while satisfying the range of motion (RoM). Video demonstration, datasets, and
additional materials are posted on our project page.
