Querying across time to interactively evolve animations

Title

Querying across time to interactively evolve animations

Creator

Schrum, Jacob

Tweraser, Isabel

Gillespie, Lauren E.

Date

2018-12-10

Date Available

2018-12-10

Date Issued

2018-07

Identifier

Isabel Tweraser, Lauren E. Gillespie, and Jacob Schrum. 2018. Querying across time to interactively evolve animations. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '18), Hernan Aguirre (Ed.). ACM, New York, NY, USA, 213-220. DOI: https://doi.org/10.1145/3205455.3205460

uri

https://collections.southwestern.edu/s/suscholar/item/241

Abstract

Compositional Pattern Producing Networks (CPPNs) are a generative encoding that has been used to evolve a variety of novel artifacts, such as 2D images, 3D shapes, audio timbres, soft robots, and neural networks. This paper takes systems that generate static 2D images and 3D shapes with CPPNs and introduces a time input, allowing each CPPN to produce a different set of results for each slice of time. Displaying the results in sequence creates smooth animations that can be interactively evolved to suit users' personal aesthetic preferences. A human subject study involving 40 individuals was conducted to demonstrate that people find the dynamic animations more complex than static outputs, and find interactive evolution of animations more enjoyable than evolution of static outputs. The novel idea of indirectly generating artifacts as a function of time could also be useful in other domains.

Language

English

Publisher

Proceedings of the Genetic and Evolutionary Computation Conference

Subject

Computing methodologies

Machine learning

Machine learning approaches

Neural networks

Bio-inspired approaches

Generative and developmental approaches

Type

Article