  We present a monocular object parsing framework for consistent keypoint
localization by capturing temporal correlation on sequential data. In this
paper, we propose a novel recurrent network based architecture to model
long-range dependencies between intermediate features which are highly useful
in tasks like keypoint localization and tracking. We leverage the
expressiveness of the popular stacked hourglass architecture and augment it by
adopting memory units between intermediate layers of the network with weights
shared across stages for video frames. We observe that this weight sharing
scheme not only enables us to frame hourglass architecture as a recurrent
network but also prove to be highly effective in producing increasingly refined
estimates for sequential tasks. Furthermore, we propose a new memory cell, we
call CoordConvGRU which learns to selectively preserve spatio-temporal
correlation and showcase our results on the keypoint localization task. The
experiments show that our approach is able to model the motion dynamics between
the frames and significantly outperforms the baseline hourglass network. Even
though our network is trained on a synthetically rendered dataset, we observe
that with minimal fine tuning on 300 real images we are able to achieve
performance at par with various state-of-the-art methods trained with the same
level of supervisory inputs. By using a simpler architecture than other methods
enables us to run it in real time on a standard GPU which is desirable for such
applications. Finally, we make our architectures and 524 annotated sequences of
cars from KITTI dataset publicly available.
