  Multi-person pose estimation in images and videos is an important yet
challenging task with many applications. Despite the large improvements in
human pose estimation enabled by the development of convolutional neural
networks, there still exist a lot of difficult cases where even the
state-of-the-art models fail to correctly localize all body joints. This
motivates the need for an additional refinement step that addresses these
challenging cases and can be easily applied on top of any existing method. In
this work, we introduce a pose refinement network (PoseRefiner) which takes as
input both the image and a given pose estimate and learns to directly predict a
refined pose by jointly reasoning about the input-output space. In order for
the network to learn to refine incorrect body joint predictions, we employ a
novel data augmentation scheme for training, where we model "hard" human pose
cases. We evaluate our approach on four popular large-scale pose estimation
benchmarks such as MPII Single- and Multi-Person Pose Estimation, PoseTrack
Pose Estimation, and PoseTrack Pose Tracking, and report systematic improvement
over the state of the art.
