  We study the problem of multi-person pose estimation in natural images. A
pose estimate describes the spatial position and identity (head, foot, knee,
etc.) of every non-occluded body part of a person. Pose estimation is difficult
due to issues such as deformation and variation in body configurations and
occlusion of parts, while multi-person settings add complications such as an
unknown number of people, with unknown appearance and possible interactions in
their poses and part locations. We give a novel integer program formulation of
the multi-person pose estimation problem, in which variables correspond to
assignments of parts in the image to poses in a two-tier, hierarchical way.
This enables us to develop an efficient custom optimization procedure based on
column generation, where columns are produced by exact optimization of very
small scale integer programs. We demonstrate improved accuracy and speed for
our method on the MPII multi-person pose estimation benchmark.
