  In this paper we propose a technique for obtaining coarse pose estimation of
humans in an image that does not require any manual supervision. While a
general unsupervised technique would fail to estimate human pose, we suggest
that sufficient information about coarse pose can be obtained by observing
human motion in multiple frames. Specifically, we consider obtaining surrogate
supervision through videos as a means for obtaining motion based grouping cues.
We supplement the method using a basic object detector that detects persons.
With just these components we obtain a rough estimate of the human pose.
  With these samples for training, we train a fully convolutional neural
network (FCNN)[20] to obtain accurate dense blob based pose estimation. We show
that the results obtained are close to the ground-truth and to the results
obtained using a fully supervised convolutional pose estimation method [31] as
evaluated on a challenging dataset [15]. This is further validated by
evaluating the obtained poses using a pose based action recognition method [5].
In this setting we outperform the results as obtained using the baseline method
that uses a fully supervised pose estimation algorithm and is competitive with
a new baseline created using convolutional pose estimation with full
supervision.
