  Pose Machines provide a sequential prediction framework for learning rich
implicit spatial models. In this work we show a systematic design for how
convolutional networks can be incorporated into the pose machine framework for
learning image features and image-dependent spatial models for the task of pose
estimation. The contribution of this paper is to implicitly model long-range
dependencies between variables in structured prediction tasks such as
articulated pose estimation. We achieve this by designing a sequential
architecture composed of convolutional networks that directly operate on belief
maps from previous stages, producing increasingly refined estimates for part
locations, without the need for explicit graphical model-style inference. Our
approach addresses the characteristic difficulty of vanishing gradients during
training by providing a natural learning objective function that enforces
intermediate supervision, thereby replenishing back-propagated gradients and
conditioning the learning procedure. We demonstrate state-of-the-art
performance and outperform competing methods on standard benchmarks including
the MPII, LSP, and FLIC datasets.
