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

• URL: http://arxiv.org/abs/2105.09936v1
• Date: Thu, 20 May 2021 17:55:31 GMT
• Title: BodyPressure -- Inferring Body Pose and Contact Pressure from a Depth Image
• Authors: Henry M. Clever, Patrick Grady, Greg Turk, and Charles C. Kemp
• Abstract summary: 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. We introduce a novel deep network that we trained on an augmented dataset and evaluated with real data.
• Score: 13.676743525349124
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• BodyMAP -- Jointly Predicting Body Mesh and 3D Applied Pressure Map for People in Bed [5.7496805993370845]
  We introduce BodyMAP, which jointly predicts the human body mesh and 3D applied pressure map across the entire human body. Our method outperforms the current state-of-the-art technique by 25% on both body mesh and 3D applied pressure map prediction tasks.
  arXiv  Detail & Related papers  (2024-04-04T03:45:17Z)
• Neural feels with neural fields: Visuo-tactile perception for in-hand manipulation [57.60490773016364]
  We combine vision and touch sensing on a multi-fingered hand to estimate an object's pose and shape during in-hand manipulation. Our method, NeuralFeels, encodes object geometry by learning a neural field online and jointly tracks it by optimizing a pose graph problem. Our results demonstrate that touch, at the very least, refines and, at the very best, disambiguates visual estimates during in-hand manipulation.
  arXiv  Detail & Related papers  (2023-12-20T22:36:37Z)
• MassNet: A Deep Learning Approach for Body Weight Extraction from A Single Pressure Image [0.41043522719179826]
  Pressure mapping mattress is a non-invasive and privacy-preserving tool to obtain the pressure distribution image over the bed surface. We propose a deep learning-based model, including a dual-branch network to extract the deep features and pose features respectively. The results show that our model outperforms the state-of-the-art algorithms over both 2 datasets.
  arXiv  Detail & Related papers  (2023-03-17T17:24:57Z)
• Detecting Human-Object Contact in Images [75.35017308643471]
  Humans constantly contact objects to move and perform tasks. There exists no robust method to detect contact between the body and the scene from an image. We build a new dataset of human-object contacts for images.
  arXiv  Detail & Related papers  (2023-03-06T18:56:26Z)
• Single-view 3D Body and Cloth Reconstruction under Complex Poses [37.86174829271747]
  We extend existing implicit function-based models to deal with images of humans with arbitrary poses and self-occluded limbs. We learn an implicit function that maps the input image to a 3D body shape with a low level of detail. We then learn a displacement map, conditioned on the smoothed surface, which encodes the high-frequency details of the clothes and body.
  arXiv  Detail & Related papers  (2022-05-09T07:34:06Z)
• Putting People in their Place: Monocular Regression of 3D People in Depth [93.70564469697095]
  Given an image with multiple people, our goal is to directly regress the pose and shape of all the people as well as their relative depth. We develop a novel method to infer the poses and depth of multiple people in a single image. We exploit a 3D body model space that lets BEV infer shapes from infants to adults.
  arXiv  Detail & Related papers  (2021-12-15T17:08:17Z)
• StereoPIFu: Depth Aware Clothed Human Digitization via Stereo Vision [54.920605385622274]
  We propose StereoPIFu, which integrates the geometric constraints of stereo vision with implicit function representation of PIFu, to recover the 3D shape of the clothed human. Compared with previous works, our StereoPIFu significantly improves the robustness, completeness, and accuracy of the clothed human reconstruction.
  arXiv  Detail & Related papers  (2021-04-12T08:41:54Z)
• Holistic 3D Human and Scene Mesh Estimation from Single View Images [5.100152971410397]
  We propose an end-to-end trainable model that perceives the 3D scene from a single RGB image. We show that our model outperforms existing human body mesh methods and indoor scene reconstruction methods.
  arXiv  Detail & Related papers  (2020-12-02T23:22:03Z)
• PLACE: Proximity Learning of Articulation and Contact in 3D Environments [70.50782687884839]
  We propose a novel interaction generation method, named PLACE, which explicitly models the proximity between the human body and the 3D scene around it. Our perceptual study shows that PLACE significantly improves the state-of-the-art method, approaching the realism of real human-scene interaction.
  arXiv  Detail & Related papers  (2020-08-12T21:00:10Z)
• Combining Implicit Function Learning and Parametric Models for 3D Human Reconstruction [123.62341095156611]
  Implicit functions represented as deep learning approximations are powerful for reconstructing 3D surfaces. Such features are essential in building flexible models for both computer graphics and computer vision. We present methodology that combines detail-rich implicit functions and parametric representations.
  arXiv  Detail & Related papers  (2020-07-22T13:46:14Z)
• Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Data [22.264931349167412]
  We describe a physics-based method that simulates human bodies at rest in a bed with a pressure sensing mat. We 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.
  arXiv  Detail & Related papers  (2020-04-02T17:44:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.