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  • Animating Athletic Motion Planning By Example

Proceedings: GI 2000

Animating Athletic Motion Planning By Example

Ronald Metoyer, Jessica Hodgins

Proceedings of Graphics Interface 2000: Montréal, Québec, Canada, 15 - 17 May 2000, 61-68

DOIhttps://doi.org/10.20380/GI2000.10
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  • Full Text
  • BibTeX

    @inproceedings@inproceedings{RMJH-gi2000,
     title = {Animating Athletic Motion Planning By Example},
     author = {Ronald Metoyer and Jessica Hodgins},
     booktitle = {Proceedings of the Graphics Interface 2000 Conference, May 15-17, 2000, Montr{'{e}}al, Qu{'{e}}bec, Canada},
     url = {http://graphicsinterface.org/wp-content/uploads/gi2000-10.pdf},
     year = {2000},
     month = {May},
     pages = {61--68}
    }
    
  • Supplementary Media

    • gi2000-10-1 (video/quicktime 3.18 MB)
      https://graphicsinterface.org/wp-content/uploads/gi2000-10-1.qt
    • gi2000-10-2 (video/quicktime 30.18 MB)
      https://graphicsinterface.org/wp-content/uploads/gi2000-10-2.qt
    • gi2000-10-3 (video/quicktime 2.52 MB)
      https://graphicsinterface.org/wp-content/uploads/gi2000-10-3.qt
    • gi2000-10-4 (video/quicktime 3.02 MB)
      https://graphicsinterface.org/wp-content/uploads/gi2000-10-4.qt
    • gi2000-10-5 (video/quicktime 2.40 MB)
      https://graphicsinterface.org/wp-content/uploads/gi2000-10-5.qt

Abstract

Character animation is usually reserved for highly skilled animators and computer programmers because few of the available tools allow the novice or casual user to create compelling animated content. In this paper, we explore a partial solution to this problem which lets the user coach animated characters by sketching their trajectories on the ground plane. The details of the motion are then computed with simulation. We create memory-based control functions for the high-level behaviors from examples supplied by the user and from real-world data of the behavior. The control function for the desired behavior is implemented through a lookup table using a K-nearest neighbor approximation algorithm. To demonstrate this approach, we present a system for defining the behaviors of defensive characters playing American football. The characters are implemented using either point-masses or dynamically simulated biped robots. We evaluate the quality of the coached behaviors by comparing the resulting trajectories to data from human players. We also assess the influence of the user's coaching examples by demonstrating that a user can construct a particular style of play.

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