Video: https://www.youtube.com/watch?v=0W8iLqHvZz8

People spend a substantial part of life at rest in bed. 3D human pose and shape estimation for this activity would have numerous beneficial applications, including patient monitoring and assistive robotics. However, 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 from a pressure image and a measured height and weight. PressureNet has a model of pressure image generation to promote consistency between estimated 3D body models and pressure image input.