  We propose a joint model of human joint detection and association for 2D
multi-person pose estimation (MPPE). The approach unifies training of joint
detection and association without a need for further processing or
sophisticated heuristics in order to associate the joints with people
individually. The approach consists of two stages, where in the first stage
joint detection heatmaps and association features are extracted, and in the
second stage, whose input are the extracted features of the first stage, we
introduce a recurrent neural network (RNN) which predicts the heatmaps of a
single person's joints in each iteration. In addition, the network learns a
stopping criterion in order to halt once it has identified all individuals in
the image. This approach allowed us to eliminate several heuristic assumptions
and parameters needed for association which do not necessarily hold true.
Additionally, such an end-to-end approach allows the final objective to be
known and directly optimized over during training. We evaluated our model on
the challenging MSCOCO dataset and obtained an improvement over the baseline,
particularly in challenging scenes with occlusions.

Computer Vision and Pattern Recognition