  Human poses that are rare or unseen in a training set are challenging for a
network to predict. Similar to the long-tailed distribution problem in visual
recognition, the small number of examples for such poses limits the ability of
networks to model them. Interestingly, local pose distributions suffer less
from the long-tail problem, i.e., local joint configurations within a rare pose
may appear within other poses in the training set, making them less rare. We
propose to take advantage of this fact for better generalization to rare and
unseen poses. To be specific, our method splits the body into local regions and
processes them in separate network branches, utilizing the property that a
joint position depends mainly on the joints within its local body region.
Global coherence is maintained by recombining the global context from the rest
of the body into each branch as a low-dimensional vector. With the reduced
dimensionality of less relevant body areas, the training set distribution
within network branches more closely reflects the statistics of local poses
instead of global body poses, without sacrificing information important for
joint inference. The proposed split-and-recombine approach, called SRNet, can
be easily adapted to both single-image and temporal models, and it leads to
appreciable improvements in the prediction of rare and unseen poses.
