  We present an approach to recover absolute 3D human poses from multi-view
images by incorporating multi-view geometric priors in our model. It consists
of two separate steps: (1) estimating the 2D poses in multi-view images and (2)
recovering the 3D poses from the multi-view 2D poses. First, we introduce a
cross-view fusion scheme into CNN to jointly estimate 2D poses for multiple
views. Consequently, the 2D pose estimation for each view already benefits from
other views. Second, we present a recursive Pictorial Structure Model to
recover the 3D pose from the multi-view 2D poses. It gradually improves the
accuracy of 3D pose with affordable computational cost. We test our method on
two public datasets H36M and Total Capture. The Mean Per Joint Position Errors
on the two datasets are 26mm and 29mm, which outperforms the state-of-the-arts
remarkably (26mm vs 52mm, 29mm vs 35mm). Our code is released at
\url{https://github.com/microsoft/multiview-human-pose-estimation-pytorch}.
