Evolving 3D Facial Expressions Using Interactive Genetic Algorithms
Autor: | Meareg A. Hailemariam, Tesfa Yohannes, Ben Goertzel |
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Rok vydání: | 2019 |
Předmět: |
Facial expression
education.field_of_study Fitness function Facial bone business.industry Computer science Crossover Population Evolutionary algorithm Pattern recognition Chromosome (genetic algorithm) Genetic algorithm Artificial intelligence business education ComputingMethodologies_COMPUTERGRAPHICS |
Zdroj: | Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ISBN: 9783030153564 |
DOI: | 10.1007/978-3-030-15357-1_40 |
Popis: | Interactive Genetic Algorithms (IGA) are applied in optimization problems where the fitness function is fuzzy or subjective. Its application transcends several domains including photography, fashion, gaming and graphics. This work introduces a novel implementation of Interactive Genetic Algorithm (IGA) for evolving facial animations on a 3D face model. In this paper, an animation of a facial expression represents a chromosome; while genes are equivalent, depending on the crossover method applied, either to a keyframe point information (f-curve) of a facial bone or f-curves of grouped sub-parts such as the head, mouth or eyes. Crossover techniques uniform, cut-and-spice, blend and their hybrids were implemented with a user playing fitness function role. Moreover, in order to maximize user preference and minimize the user fatigue during evolution, sub-parts based elitism was implemented. Subjective measurements of credibility and peculiarity parameters among a given artist animated and evolved expressions were done. For the experiment results here, an average crossover percentage of 85%, a mutation level of 0.01, initial population of 36, and 8 rounds of evolution settings were considered. As detailed in the experiment section, the IGA based evolved facial expressions scored competitive results to the artist-animated ones. |
Databáze: | OpenAIRE |
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