# BodyPressure -- Inferring Body Pose and Contact Pressure from a Depth Image

arXiv: https://arxiv.org/abs/2105.09936 · HTML: https://mlsanity.com/paper/2105.09936/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 49 of its 87 references that are in the map.
- First posted on arXiv 20 May 2021.

## Builds on (most cited first, 30 of 49)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [A Point Set Generation Network for 3D Object Reconstruction from a Single Image](https://mlsanity.com/paper/1612.00603/) (2016)
- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image](https://mlsanity.com/paper/1607.08128/) (2016)
- [Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop](https://mlsanity.com/paper/1909.12828/) (2019)
- [Semantic Scene Completion from a Single Depth Image](https://mlsanity.com/paper/1611.08974/) (2016)
- [Learning joint reconstruction of hands and manipulated objects](https://mlsanity.com/paper/1904.05767/) (2019)
- [Learning from Synthetic Humans](https://mlsanity.com/paper/1701.01370/) (2017)
- [Sim-to-Real: Learning Agile Locomotion For Quadruped Robots](https://mlsanity.com/paper/1804.10332/) (2018)
- [An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution](https://mlsanity.com/paper/1807.03247/) (2018)
- [HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation](https://mlsanity.com/paper/2011.14672/) (2020)
- [Resolving 3D Human Pose Ambiguities with 3D Scene Constraints](https://mlsanity.com/paper/1908.06963/) (2019)
- [Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose](https://mlsanity.com/paper/1611.07828/) (2016)
- [Deep Learning-Based Human Pose Estimation: A Survey](https://mlsanity.com/paper/2012.13392/) (2020)
- [Monocular Expressive Body Regression through Body-Driven Attention](https://mlsanity.com/paper/2008.09062/) (2020)
- [ContactOpt: Optimizing Contact to Improve Grasps](https://mlsanity.com/paper/2104.07267/) (2021)
- [Sim-to-Real Reinforcement Learning for Deformable Object Manipulation](https://mlsanity.com/paper/1806.07851/) (2018)
- [Generating 3D People in Scenes without People](https://mlsanity.com/paper/1912.02923/) (2019)
- [Human Pose Estimation with Iterative Error Feedback](https://mlsanity.com/paper/1507.06550/) (2015)
- [LEAP: Learning Articulated Occupancy of People](https://mlsanity.com/paper/2104.06849/) (2021)
- [Structured Prediction Helps 3D Human Motion Modelling](https://mlsanity.com/paper/1910.09070/) (2019)
- [On Self-Contact and Human Pose](https://mlsanity.com/paper/2104.03176/) (2021)
- [Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation](https://mlsanity.com/paper/1804.01110/) (2018)
- [Synthesizing Training Images for Boosting Human 3D Pose Estimation](https://mlsanity.com/paper/1604.02703/) (2016)
- [Human pose estimation via Convolutional Part Heatmap Regression](https://mlsanity.com/paper/1609.01743/) (2016)
- [Assistive Gym: A Physics Simulation Framework for Assistive Robotics](https://mlsanity.com/paper/1910.04700/) (2019)
- [SimulCap : Single-View Human Performance Capture with Cloth Simulation](https://mlsanity.com/paper/1903.06323/) (2019)
- [Deep Kinematic Pose Regression](https://mlsanity.com/paper/1609.05317/) (2016)

## Built on by (most cited first, 20 of 20)

- [Object Detection Using Sim2Real Domain Randomization for Robotic Applications](https://mlsanity.com/paper/2208.04171/) (2022)
- [PressureVision: Estimating Hand Pressure from a Single RGB Image](https://mlsanity.com/paper/2203.10385/) (2022)
- [Self-supervised Human Mesh Recovery with Cross-Representation Alignment](https://mlsanity.com/paper/2209.04596/) (2022)
- [Anatomy-guided domain adaptation for 3D in-bed human pose estimation](https://mlsanity.com/paper/2211.12193/) (2022)
- [PresSim: An End-to-end Framework for Dynamic Ground Pressure Profile Generation from Monocular Videos Using Physics-based 3D Simulation](https://mlsanity.com/paper/2302.00391/) (2023)
- [DICE: End-to-end Deformation Capture of Hand-Face Interactions from a Single Image](https://mlsanity.com/paper/2406.17988/) (2024)
- [Under the Cover Infant Pose Estimation using Multimodal Data](https://mlsanity.com/paper/2210.00662/) (2022)
- [DiSRT-In-Bed: Diffusion-Based Sim-to-Real Transfer Framework for In-Bed Human Mesh Recovery](https://mlsanity.com/paper/2504.03006/) (2025)
- [BodyMAP -- Jointly Predicting Body Mesh and 3D Applied Pressure Map for People in Bed](https://mlsanity.com/paper/2404.03183/) (2024)
- [Text me the data: Generating Ground Pressure Sequence from Textual Descriptions for HAR](https://mlsanity.com/paper/2402.14427/) (2024)
- [BlanketGen - A synthetic blanket occlusion augmentation pipeline for MoCap datasets](https://mlsanity.com/paper/2210.12035/) (2022)
- [PressureTransferNet: Human Attribute Guided Dynamic Ground Pressure Profile Transfer using 3D simulated Pressure Maps](https://mlsanity.com/paper/2308.00538/) (2023)
- [TxP: Reciprocal Generation of Ground Pressure Dynamics and Activity Descriptions for Improving Human Activity Recognition](https://mlsanity.com/paper/2505.02052/) (2025)
- [PressMimic: Pressure-Guided Motion Capture and Control for Humanoid Robot Imitation](https://mlsanity.com/paper/2606.26741/) (2026)
- [BlanketGen2-Fit3D: Synthetic Blanket Augmentation Towards Improving Real-World In-Bed Blanket Occluded Human Pose Estimation](https://mlsanity.com/paper/2501.12318/) (2025)
- [Multi-modal 3D Pose and Shape Estimation with Computed Tomography](https://mlsanity.com/paper/2503.19405/) (2025)
- [ChairPose: Pressure-based Chair Morphology Grounded Sitting Pose Estimation through Simulation-Assisted Training](https://mlsanity.com/paper/2508.01850/) (2025)
- [Pressure2Motion: Hierarchical Human Motion Reconstruction from Ground Pressure with Text Guidance](https://mlsanity.com/paper/2511.05038/) (2025)
- [Improving the Plausibility of Pressure Distributions Synthesized from Depth Image through Generative Modeling](https://mlsanity.com/paper/2512.13757/) (2025)
- [Patient4D: Temporally Consistent Patient Body Mesh Recovery from Monocular Operating Room Video](https://mlsanity.com/paper/2603.17178/) (2026)

## Abstract (arXiv)

Contact pressure between the human body and its surroundings has important implications. For example, it plays a role in comfort, safety, posture, and health. We present a method that infers contact pressure between a human body and a mattress from a depth image. Specifically, we focus on using a depth image from a downward facing camera to infer pressure on a body at rest in bed occluded by bedding, which is directly applicable to the prevention of pressure injuries in healthcare. Our approach involves augmenting a real dataset with synthetic data generated via a soft-body physics simulation of a human body, a mattress, a pressure sensing mat, and a blanket. We introduce a novel deep network that we trained on an augmented dataset and evaluated with real data. The network contains an embedded human body mesh model and uses a white-box model of depth and pressure image generation. Our network successfully infers body pose, outperforming prior work. It also infers contact pressure across a 3D mesh model of the human body, which is a novel capability, and does so in the presence of occlusion from blankets.