  Human pose estimation - the process of recognizing a human's limb positions
and orientations in a video - has many important applications including
surveillance, diagnosis of movement disorders, and computer animation. While
deep learning has lead to great advances in 2D and 3D pose estimation from
single video sources, the problem of estimating 3D human pose from multiple
video sensors with overlapping fields of view has received less attention. When
the application allows use of multiple cameras, 3D human pose estimates may be
greatly improved through fusion of multi-view pose estimates and observation of
limbs that are fully or partially occluded in some views. Past approaches to
multi-view 3D pose estimation have used probabilistic graphical models to
reason over constraints, including per-image pose estimates, temporal
smoothness, and limb length. In this paper, we present a pipeline for
multi-view 3D pose estimation of multiple individuals which combines a
state-of-art 2D pose detector with a factor graph of 3D limb constraints
optimized with belief propagation. We evaluate our results on the TUM-Campus
and Shelf datasets for multi-person 3D pose estimation and show that our system
significantly out-performs the previous state-of-the-art with a simpler model
of limb dependency.

Computer Vision and Pattern Recognition