Interactive Evolution of Particle Systems for Computer Graphics and Animation

Interactive Evolutionary Computation (IEC) creates the intriguing possibility that a large variety of useful content can be produced quickly and easily for practical computer graphics and gaming applications. To show that IEC can produce such content, this paper applies IEC to particle system effect...

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Veröffentlicht in:IEEE transactions on evolutionary computation Jg. 13; H. 2; S. 418 - 432
Hauptverfasser: Hastings, E.J., Guha, R.K., Stanley, K.O.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.04.2009
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Animation
Application software
Applied sciences
Artificial intelligence
Artificial neural networks
Atoms & subatomic particles
Computer graphics
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Connectionism. Neural networks
Evolutionary computation
Exact sciences and technology
IEC
Interactive evolutionary computation (IEC)
Learning and adaptive systems
Learning theory
Mathematical analysis
Mathematical models
Mathematical programming
Neural networks
neuroevolution of augmenting topologies (NEAT)
particle systems
Pattern recognition. Digital image processing. Computational geometry
Power generation
Programming
Projectiles
Software
Space exploration
Special effects
Software maintenance
Evolutionary algorithm
Neural network
Topology
Computer animation
Interactive evolutionary computation (IEC)
Weapon
Projectile
Interactive system
Video game
Fires
Computer graphics
neuroevolution of augmenting topologies (NEAT)
Graphic method
particle systems
Mathematical programming
ISSN:1089-778X, 1941-0026
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Zusammenfassung:Interactive Evolutionary Computation (IEC) creates the intriguing possibility that a large variety of useful content can be produced quickly and easily for practical computer graphics and gaming applications. To show that IEC can produce such content, this paper applies IEC to particle system effects, which are the de facto method in computer graphics for generating fire, smoke, explosions, electricity, water, and many other special effects. While particle systems are capable of producing a broad array of effects, they require substantial mathematical and programming knowledge to produce. Therefore, efficient particle system generation tools are required for content developers to produce special effects in a timely manner. This paper details the design, representation, and animation of particle systems via two IEC tools called NEAT Particles and NEAT Projectiles. Both tools evolve artificial neural networks (ANN) with the NeuroEvolution of Augmenting Topologies (NEAT) method to control the behavior of particles. NEAT Particles evolves general-purpose particle effects, whereas NEAT Projectiles specializes in evolving particle weapon effects for video games. The primary advantage of this NEAT-based IEC approach is to decouple the creation of new effects from mathematics and programming, enabling content developers without programming knowledge to produce complex effects. Furthermore, it allows content designers to produce a broader range of effects than typical development tools. Finally, it acts as a concept generator, allowing content creators to interactively and efficiently explore the space of possible effects. Both NEAT Particles and NEAT Projectiles demonstrate how IEC can evolve useful content for graphical media and games, and are together a step toward the larger goal of automated content generation.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2008.2004261