"Distributed intelligence: exploring federated paradigms across computi" by Yang Deng

Document Type

Dissertation

Date of Award

12-31-2023

Degree Name

Doctor of Philosophy in Electrical Engineering - (Ph.D.)

Department

Electrical and Computer Engineering

First Advisor

Tao Han

Second Advisor

Nirwan Ansari

Third Advisor

Abdallah Khreishah

Fourth Advisor

Nakjung Choi

Fifth Advisor

Yu Wang

Abstract

Computer Graphics (CG) revolves around virtual content creation using computational methods, spanning applications from games to visual effects. Typically, the creation of CG content is led by expert practitioners who guide computational algorithms towards satisfactory results. Thus, creating CG content often requires manual iterations encompassing algorithm design, parameter tuning, and aesthetic feedback. This work investigates how to leverage crowd-sourcing to streamline such creation processes, focusing on animation and simulation. In animation, a novel crowd-sourcing framework is proposed for combat animation, enabling users to analyze motion similarities, and retrieve matching motions using novel crowd-sourced motion features. Such features enable quantifying previously intractable definitions such as if a motion is entertaining or powerful. Similarly, in simulation, a crowd-sourced Bayesian optimization framework is proposed for creating natural-looking virtual crowds, demonstrating qualitative and quantitative improvements over prior methods. The presentation concludes with a discussion on the limitations of this approach, as well as potential future work.

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