  We consider the task of learning to estimate human pose in still images. In
order to avoid the high cost of full supervision, we propose to use a diverse
data set, which consists of two types of annotations: (i) a small number of
images are labeled using the expensive ground-truth pose; and (ii) other images
are labeled using the inexpensive action label. As action information helps
narrow down the pose of a human, we argue that this approach can help reduce
the cost of training without significantly affecting the accuracy. To
demonstrate this we design a probabilistic framework that employs two
distributions: (i) a conditional distribution to model the uncertainty over the
human pose given the image and the action; and (ii) a prediction distribution,
which provides the pose of an image without using any action information. We
jointly estimate the parameters of the two aforementioned distributions by
minimizing their dissimilarity coefficient, as measured by a task-specific loss
function. During both training and testing, we only require an efficient
sampling strategy for both the aforementioned distributions. This allows us to
use deep probabilistic networks that are capable of providing accurate pose
estimates for previously unseen images. Using the MPII data set, we show that
our approach outperforms baseline methods that either do not use the diverse
annotations or rely on pointwise estimates of the pose.
