  Accurately predicting the 3D human posture and the pressure exerted on the
body for people resting in bed, visualized as a body mesh (3D pose & shape)
with a 3D pressure map, holds significant promise for healthcare applications,
particularly, in the prevention of pressure ulcers. Current methods focus on
singular facets of the problem -- predicting only 2D/3D poses, generating 2D
pressure images, predicting pressure only for certain body regions instead of
the full body, or forming indirect approximations to the 3D pressure map. In
contrast, we introduce BodyMAP, which jointly predicts the human body mesh and
3D applied pressure map across the entire human body. Our network leverages
multiple visual modalities, incorporating both a depth image of a person in bed
and its corresponding 2D pressure image acquired from a pressure-sensing
mattress. The 3D pressure map is represented as a pressure value at each mesh
vertex and thus allows for precise localization of high-pressure regions on the
body. Additionally, we present BodyMAP-WS, a new formulation of pressure
prediction in which we implicitly learn pressure in 3D by aligning sensed 2D
pressure images with a differentiable 2D projection of the predicted 3D
pressure maps. In evaluations with real-world human data, our method
outperforms the current state-of-the-art technique by 25% on both body mesh and
3D applied pressure map prediction tasks for people in bed.
