We propose to estimate 3D human pose from multi-view images and a few IMUs
attached at person's limbs. It operates by firstly detecting 2D poses from the
two signals, and then lifting them to the 3D space. We present a geometric
approach to reinforce the visual features of each pair of joints based on the
IMUs. This notably improves 2D pose estimation accuracy especially when one
joint is occluded. We call this approach Orientation Regularized Network (ORN).
Then we lift the multi-view 2D poses to the 3D space by an Orientation
Regularized Pictorial Structure Model (ORPSM) which jointly minimizes the
projection error between the 3D and 2D poses, along with the discrepancy
between the 3D pose and IMU orientations. The simple two-step approach reduces
the error of the state-of-the-art by a large margin on a public dataset. Our
code will be released at this https URL

采用几个附着在人体肢体上的 IMUs 和多视图图像估计人类的 3D 姿态并提出了一种基于几何学的方法，用于增强每对关节的视觉特征并使用它们将多视图 2D 姿势提升到 3D 空间中，这种方法称为方向正则化网络（ORN）。