# PressureVision: Estimating Hand Pressure from a Single RGB Image

arXiv: https://arxiv.org/abs/2203.10385 · HTML: https://mlsanity.com/paper/2203.10385/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 75 references that are in the map.
- First posted on arXiv 19 Mar 2022.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [GRAB: A Dataset of Whole-Body Human Grasping of Objects](https://mlsanity.com/paper/2008.11200/) (2020)
- [Learning joint reconstruction of hands and manipulated objects](https://mlsanity.com/paper/1904.05767/) (2019)
- [First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations](https://mlsanity.com/paper/1704.02463/) (2017)
- [ContactPose: A Dataset of Grasps with Object Contact and Hand Pose](https://mlsanity.com/paper/2007.09545/) (2020)
- [Grasping Field: Learning Implicit Representations for Human Grasps](https://mlsanity.com/paper/2008.04451/) (2020)
- [FrankMocap: A Monocular 3D Whole-Body Pose Estimation System via Regression and Integration](https://mlsanity.com/paper/2108.06428/) (2021)
- [ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging](https://mlsanity.com/paper/1904.06830/) (2019)
- [Leveraging Photometric Consistency over Time for Sparsely Supervised Hand-Object Reconstruction](https://mlsanity.com/paper/2004.13449/) (2020)
- [ContactOpt: Optimizing Contact to Improve Grasps](https://mlsanity.com/paper/2104.07267/) (2021)
- [Generating 3D People in Scenes without People](https://mlsanity.com/paper/1912.02923/) (2019)
- [Capturing Hands in Action using Discriminative Salient Points and Physics Simulation](https://mlsanity.com/paper/1506.02178/) (2015)
- [ContactGrasp: Functional Multi-finger Grasp Synthesis from Contact](https://mlsanity.com/paper/1904.03754/) (2019)
- [Accuracy comparison across face recognition algorithms: Where are we on measuring race bias?](https://mlsanity.com/paper/1912.07398/) (2019)
- [Estimating 3D Motion and Forces of Person-Object Interactions from Monocular Video](https://mlsanity.com/paper/1904.02683/) (2019)
- [Use the Force, Luke! Learning to Predict Physical Forces by Simulating Effects](https://mlsanity.com/paper/2003.12045/) (2020)
- [Detecting Hands and Recognizing Physical Contact in the Wild](https://mlsanity.com/paper/2010.09676/) (2020)
- [ReSkin: versatile, replaceable, lasting tactile skins](https://mlsanity.com/paper/2111.00071/) (2021)
- [Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Data](https://mlsanity.com/paper/2004.01166/) (2020)
- [BodyPressure -- Inferring Body Pose and Contact Pressure from a Depth Image](https://mlsanity.com/paper/2105.09936/) (2021)
- [Estimating Fingertip Forces, Torques, and Local Curvatures from Fingernail Images](https://mlsanity.com/paper/1909.05659/) (2019)

## Built on by (most cited first, 23 of 23)

- [ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation](https://mlsanity.com/paper/2204.13662/) (2022)
- [DECO: Dense Estimation of 3D Human-Scene Contact In The Wild](https://mlsanity.com/paper/2309.15273/) (2023)
- [GigaHands: A Massive Annotated Dataset of Bimanual Hand Activities](https://mlsanity.com/paper/2412.04244/) (2024)
- [EgoPressure: A Dataset for Hand Pressure and Pose Estimation in Egocentric Vision](https://mlsanity.com/paper/2409.02224/) (2024)
- [OPENTOUCH: Bringing Full-Hand Touch to Real-World Interaction](https://mlsanity.com/paper/2512.16842/) (2025)
- [PressureVision++: Estimating Fingertip Pressure from Diverse RGB Images](https://mlsanity.com/paper/2301.02310/) (2023)
- [EgoTouch: On-Body Touch Input Using AR/VR Headset Cameras](https://mlsanity.com/paper/2509.01786/) (2025)
- [HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments](https://mlsanity.com/paper/2604.14944/) (2026)
- [EgoTactile: Learning Grasp Pressure for Everyday Objects from Egocentric Video](https://mlsanity.com/paper/2606.09243/) (2026)
- [Posture-Informed Muscular Force Learning for Robust Hand Pressure Estimation](https://mlsanity.com/paper/2410.23629/) (2024)
- [Glove2Hand: Synthesizing Natural Hand-Object Interaction from Multi-Modal Sensing Gloves](https://mlsanity.com/paper/2603.20850/) (2026)
- [ACE-Data-0: Human-Centric Ambient Capture as Embodied Data Engine](https://mlsanity.com/paper/2607.28625/) (2026)
- [HOPE: Hand-Object Pressure Estimation from Monocular Videos](https://mlsanity.com/paper/2608.06192/) (2026)
- [EgoTac: In-the-wild Tactile Prediction from Egocentric Vision](https://mlsanity.com/paper/2608.15060/) (2026)
- [DexCanvas: Bridging Human Demonstrations and Robot Learning for Dexterous Manipulation](https://mlsanity.com/paper/2510.15786/) (2025)
- [EgoPressDiff: Multimodal Video Diffusion for Egocentric UV-Domain Hand-Pressure Estimation](https://mlsanity.com/paper/2606.06872/) (2026)
- [TouchSight: Bare-Handed Tactile Prediction from Egocentric Video via Generative Visual Augmentation](https://mlsanity.com/paper/2609.20414/) (2026)
- [EclipseTouch: Touch Segmentation on Ad Hoc Surfaces using Worn Infrared Shadow Casting](https://mlsanity.com/paper/2509.03430/) (2025)
- [Improving the Plausibility of Pressure Distributions Synthesized from Depth Image through Generative Modeling](https://mlsanity.com/paper/2512.13757/) (2025)
- [LEXIS: LatEnt ProXimal Interaction Signatures for 3D HOI from an Image](https://mlsanity.com/paper/2604.20800/) (2026)
- [EgoPHI: Estimating 3D Hand-Object Contact and Force from Egocentric Vision](https://mlsanity.com/paper/2608.13014/) (2026)
- [Vision-Language Models for Occupational Physical Exposure Assessment: Estimating External Hand Forces in Manual Material Handling Tasks from RGB Video](https://mlsanity.com/paper/2608.22586/) (2026)
- [TouchScale: 500 Hours of Human Vision and Touch for Visual-Tactile Learning](https://mlsanity.com/paper/2610.10288/) (2026)

## Abstract (arXiv)

People often interact with their surroundings by applying pressure with their hands. While hand pressure can be measured by placing pressure sensors between the hand and the environment, doing so can alter contact mechanics, interfere with human tactile perception, require costly sensors, and scale poorly to large environments. We explore the possibility of using a conventional RGB camera to infer hand pressure, enabling machine perception of hand pressure from uninstrumented hands and surfaces. The central insight is that the application of pressure by a hand results in informative appearance changes. Hands share biomechanical properties that result in similar observable phenomena, such as soft-tissue deformation, blood distribution, hand pose, and cast shadows. We collected videos of 36 participants with diverse skin tone applying pressure to an instrumented planar surface. We then trained a deep model (PressureVisionNet) to infer a pressure image from a single RGB image. Our model infers pressure for participants outside of the training data and outperforms baselines. We also show that the output of our model depends on the appearance of the hand and cast shadows near contact regions. Overall, our results suggest the appearance of a previously unobserved human hand can be used to accurately infer applied pressure. Data, code, and models are available online.