  Humans spend vast hours in bed -- about one-third of the lifetime on average.
Besides, a human at rest is vital in many healthcare applications. Typically,
humans are covered by a blanket when resting, for which we propose a multimodal
approach to uncover the subjects so their bodies at rest can be viewed without
the occlusion of the blankets above. We propose a pyramid scheme to effectively
fuse the different modalities in a way that best leverages the knowledge
captured by the multimodal sensors. Specifically, the two most informative
modalities (i.e., depth and infrared images) are first fused to generate good
initial pose and shape estimation. Then pressure map and RGB images are further
fused one by one to refine the result by providing occlusion-invariant
information for the covered part, and accurate shape information for the
uncovered part, respectively. However, even with multimodal data, the task of
detecting human bodies at rest is still very challenging due to the extreme
occlusion of bodies. To further reduce the negative effects of the occlusion
from blankets, we employ an attention-based reconstruction module to generate
uncovered modalities, which are further fused to update current estimation via
a cyclic fashion. Extensive experiments validate the superiority of the
proposed model over others.
