  Recent advances in deep pose estimation models have proven to be effective in
a wide range of applications such as health monitoring, sports, animations, and
robotics. However, pose estimation models fail to generalize when facing images
acquired from in-bed pressure sensing systems. In this paper, we address this
challenge by presenting a novel end-to-end framework capable of accurately
locating body parts from vague pressure data. Our method exploits the idea of
equipping an off-the-shelf pose estimator with a deep trainable neural network,
which pre-processes and prepares the pressure data for subsequent pose
estimation. Our model transforms the ambiguous pressure maps to images
containing shapes and structures similar to the common input domain of the
pre-existing pose estimation methods. As a result, we show that our model is
able to reconstruct unclear body parts, which in turn enables pose estimators
to accurately and robustly estimate the pose. We train and test our method on a
manually annotated public pressure map dataset using a combination of loss
functions. Results confirm the effectiveness of our method by the high visual
quality in the generated images and the high pose estimation rates achieved.
