  Deep ConvNets have been shown to be effective for the task of human pose
estimation from single images. However, several challenging issues arise in the
video-based case such as self-occlusion, motion blur, and uncommon poses with
few or no examples in training data sets. Temporal information can provide
additional cues about the location of body joints and help to alleviate these
issues. In this paper, we propose a deep structured model to estimate a
sequence of human poses in unconstrained videos. This model can be efficiently
trained in an end-to-end manner and is capable of representing appearance of
body joints and their spatio-temporal relationships simultaneously. Domain
knowledge about the human body is explicitly incorporated into the network
providing effective priors to regularize the skeletal structure and to enforce
temporal consistency. The proposed end-to-end architecture is evaluated on two
widely used benchmarks (Penn Action dataset and JHMDB dataset) for video-based
pose estimation. Our approach significantly outperforms the existing
state-of-the-art methods.
