People spend a substantial part of their lives at rest in bed. 3D human pose
and shape estimation for this activity would have numerous beneficial
applications, yet line-of-sight perception is complicated by occlusion from
bedding. Pressure sensing mats are a promising alternative, but training data
is challenging to collect at scale. We describe a physics-based method that
simulates human bodies at rest in a bed with a pressure sensing mat, and
present PressurePose, a synthetic dataset with 206K pressure images with 3D
human poses and shapes. We also present PressureNet, a deep learning model that
estimates human pose and shape given a pressure image and gender. PressureNet
incorporates a pressure map reconstruction (PMR) network that models pressure
image generation to promote consistency between estimated 3D body models and
pressure image input. In our evaluations, PressureNet performed well with real
data from participants in diverse poses, even though it had only been trained
with synthetic data. When we ablated the PMR network, performance dropped
substantially.

利用由物理学建模的合成数据集，提出了一个使用压力传感垫推断人体姿势和形状的深度学习模型 Pressurepose，并展示了这个模型的良好性能。