Document Type

Thesis

Date of Degree

Fall 2014

Degree Name

MS (Master of Science)

Degree In

Electrical and Computer Engineering

First Advisor

Karim Abdel-Malek

Second Advisor

Guadalupe Canahuate

Abstract

Simulating realistic human behavior on a virtual avatar presents a difficult task. Because the simulated environment does not adhere to the same scientific principles that we do in the existent world, the avatar becomes capable of achieving infeasible postures. In an attempt to obtain realistic human simulation, real world constraints are imposed onto the non-sentient being. One such constraint, and the topic of this thesis, is self-collision avoidance. For the purposes of this topic, a posture will be defined solely as a collection of angles formed by each joint on the avatar. The goal of self-collision avoidance is to eliminate the formation of any posture where multiple body parts are attempting to occupy the exact same space. My work necessitates an extension of this definition to also include collision avoidance with objects attached to the body, such as a backpack or armor. In order to prevent these collisions from occurring, I have implemented an effort-based approach for correcting afflicted postures. This technique specifically pertains to postures that are sequenced together with the objective of animating the avatar. As such, the animation's coherence and defining characteristics must be preserved. My approach to this problem is unique in that it strategically blends the concept of keyframe interpolation with an optimization-based strategy for posture prediction. Although there has been considerable work done with methods for keyframe interpolation, there has been minimal progress towards integrating a realistic collision response strategy. Additionally, I will test this optimization-based approach through the use of a complex kinematic human model and investigate the use of the results as input to an existing dynamic motion prediction system.

Public Abstract

The research presented in this thesis provides a strategy for implementing realistic self-collision avoidance on a virtual avatar. The goal of this self-collision avoidance method is to prevent multiple parts of the body from occupying the exact same space. This method not only accounts for limbs of the body, but is also dynamic enough to account for clothing and equipment that is placed on the avatar. This strategy can be of great use when animating any articulated figure in a virtual environment and is targeted specifically for the fields of animation and digital human modeling.

Keywords

publicabstract, Digital Human Modeling, Keyframe Interpolation, Kinematics, Optimization-based Posture Prediction, Predictive Dynamics, Self-Collision Avoidance

Pages

x, 77 pages

Bibliography

Includes bibliographical references (pages 55-57).

Copyright

Copyright 2014 Richard Kennedy Degenhardt

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