English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Data