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Measuring, modelling, simulating, and predicting human tissue properties

University of British Columbia

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.

Text

2021-06-30

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/72777

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/