Measuring, modelling, simulating, and predicting human tissue properties
department or school: Science, Faculty of
department or school: Computer Science, Department of
data provider: DSpace
degree campus: UBCV
creator: Rothwell, Austin
issued: 2021-06-30T07:00:00Z
issued: 2019
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Personalized simulation of human bodies is a long standing goal in many applications, ranging from animation to apparel. Even though personalized geometric models can now be easily acquired, physics-based simulation requires soft tissue properties and their distribution over the person’s body. Here we show that mechanical properties of the human body can be directly measured using a novel hand-held device. We describe a complete pipeline for measurement, modeling, parameter estimation, and simulation. The methods described here can be used to create personalized models of an individual human or of a population. Furthermore, we show how to predict soft tissue properties from widely available 3D geometric models of the human body. To train such a prediction model, we utilize a unique database of registered measurements of body shape and soft tissue properties, acquired from over 70 participants. We use a recently introduced convolutional neural network architecture adapted for 3D surfaces, and train the network to predict the distribution of tissue properties over the 3D human body surface. Once the network is trained, no specialized equipment is required, and soft tissue properties are predicted in minutes. The method can be used with commodity 3D scanners, and even with geometric models downloaded from Internet or created by artists. Our methods make realistic human body simulations available to a wide range of users and applications.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/72777
note: MEASURING, MODELLING, SIMULATING, ANDPREDICTING HUMAN TISSUE PROPERTIESbyAustin RothwellBA HONS Psychology, Minor in Computer Science, University of BritishColumbia, 2017A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinTHE FACULTY OF GRADUATE AND POSTDOCTORALSTUDIES(Computer Science)The University of British Columbia(Vancouver)December 2019c© Austin Rothwell, 2019The following individuals certify that they have read, and recommend to the Fac-ulty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled:MEASURING, MODELLING, SIMULATING, AND PREDICTINGHUMAN TISSUE PROPERTIESsubmitted by Austin Rothwell in partial fulfillment of the requirements for thedegree of MASTER OF SCIENCE in Computer Science.Examining Committee:Dinesh Pai, Computer ScienceSupervisorLeon Sigal, Computer ScienceExamining Committee MemberiiAbstractPersonalized simulation of human bodies is a long standing goal in many appli-cations, ranging from animation to apparel. Even though personalized geometricmodels can now be easily acquired, physics-based simulation requires soft tissueproperties and their distribution over the person’s body. Here we show that me-chanical properties of the human body can be directly measured using a novelhand-held device. We describe a complete pipeline for measurement, modeling,parameter estimation, and simulation. The methods described here can be usedto create personalized models of an individual human or of a population. Fur-thermore, we show how to predict soft tissue properties from widely available 3Dgeometric models of the human body. To train such a prediction model, we utilizea unique database of registered measurements of body shape and soft tissue prop-erties, acquired from over 70 participants. We use a recently introduced convolu-tional neural network architecture adapted for 3D surfaces, and train the networkto predict the distribution of tissue properties over the 3D human body surface.Once t…
has type: Thesis/Dissertation
date issued: 2020-05
is shown at: 10.14288/1.0387124
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/72777