Optimising Kinematic Systems Using Crowd-Sourcing and Genetic Algorithms

Abstract

Procedural animation systems are capable of synthesising life-like organic motion automatically. However due to extensive parameterisation, tuning these systems can be very difficult. Not only are there potentially hundreds of interlinked parameters, the resultant animation can be very subjective, and the process is difficult to automate effectively.
The research presented in this thesis is divided into three stages. Our first motivation is to examine whether artificially intelligent characters appear more or less human-like in virtual reality (VR). Our results indicate that there is a clear split in how we perceive an artificial character depending on viewing method and game type.
Our second motivation is to assess whether anonymous individuals can anneal a
procedurally animated creature towards a desired outcome. To do this we present an online system which used crowd-sourcing to direct a genetic algorithm. This methodology is further tested by asking users to interactively rate a population of virtual dolphins to a prescribed behavioural criterion. Our results show that within a few generations a group of users can successfully tune an animation system toward a desired behaviour.
Our final motivation is to investigate if there are differences in animation and behavioural preference between observations made across 2D screens and VR. We describe a study where users tuned two sets of dolphin animation systems in parallel, one using a normal monitor and another using an Oculus Rift. Our results indicate that being immersed in VR leads to some key differences in preferred behaviour.

Date of Award	17 Jun 2019
Original language	English
Awarding Institution	Bangor University
Sponsors	HPC Wales & THE LEVERHULME TRUST
Supervisor	Llyr Ap Cenydd (Supervisor) & William Teahan (Supervisor)