Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


finalDraft.pdf (19.71 MB)

Supplemental Files

File List


BaselineVsNew.mp4 (9.26 MB)

GhostsVsStreamlines.mp4 (17.54 MB)

GridVsSorted.mp4 (8.8 MB)

PlayAllVsPlaySome.mp4 (9.2 MB)

TraceDemo.mp4 (36.89 MB)

ETD Abstract Container

Abstract Header

Interactive Evolutionary Design with Region-of-Interest Selection for Spatiotemporal Ideation & Generation

Eisenmann, Jonathan A
http://rave.ohiolink.edu/etdc/view?acc_num=osu1405610355

Abstract Details

2014, Doctor of Philosophy, Ohio State University, Computer Science and Engineering.
In computer graphics, digital asset creation is still an important and challenging problem. The generation of diverse and interesting sets of assets for narratives and interactive environments is largely a creative task, but requires a significant amount of technical busywork. Interactive evolutionary design tools seem like a promising solution because they enable human intuition and creative decision making in the context of high-dimensional design domains while leaving the busywork to the computer. However, they have yet to prove themselves useful in a real-world production scenario, in part due to the human fatigue bottleneck. Current evolutionary algorithms for interactive design systems only receive feedback about candidate fitness at the whole-candidate level. The goal of this research is to make interactive evolutionary algorithms suitable for the ideation of spatiotemporal digital assets by enhancing the steering available to the designer at each step by enabling fitness feedback at the component level. This will promote faster iteration on ideas, making it possible for the designer to spend more time evaluating complex candidates or exploring larger design search spaces with additional interesting variety, without increasing the time to satisfactory convergence. The new system accomplishes this in a model independent way and without the need for neural network training by integrating sensitivity analysis into the evolutionary algorithm. Sensitivity analysis is a set of statistical analysis methods that attribute variation in model output to specific model parameters. Resulting sensitivities are incorporated into the reproduction operators of the evolutionary algorithm to enhance the search trajectory navigation. The system also incorporates strategies to reduce the designer's visual load when viewing large arrays of time-varying data as well as interaction methods for selecting regions of movement through both space and time. Qualitative visual results and quantitative measurements are provided, where appropriate, to validate the functionality of each system component. Overall system usability is validated using case studies where designers make use of the tool in different contexts. The designer feedback and narratives about experiences with the system show that interactive evolutionary algorithms can be made suitable for the ideation of digital assets, even in spatiotemporal domains such as character animation.
Rick Parent (Advisor)
Matthew Lewis (Committee Member)
Han-Wei Shen (Committee Chair)
183 p.
Computer Science; Design
human computer interface; interactive evolution; evolutionary computation; computational design; sensitivity analysis; region of interest selection; character animation; generative; fatigue; crossover; mutation; interaction; digital assets; diversity

Recommended Citations

Citations

  • Eisenmann, J. A. (2014). Interactive Evolutionary Design with Region-of-Interest Selection for Spatiotemporal Ideation & Generation [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1405610355

    APA Style (7th edition)

  • Eisenmann, Jonathan. Interactive Evolutionary Design with Region-of-Interest Selection for Spatiotemporal Ideation & Generation. 2014. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1405610355.

    MLA Style (8th edition)

  • Eisenmann, Jonathan. "Interactive Evolutionary Design with Region-of-Interest Selection for Spatiotemporal Ideation & Generation." Doctoral dissertation, Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1405610355

    Chicago Manual of Style (17th edition)

osu1405610355
339
© 2014, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.