Evolutionary Controller Synthesis for 3-D Character Animation

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@PhdThesis{gritz:dissertation,
  author =       "Larry Israel Gritz",
  title =        "Evolutionary Controller Synthesis for 3-D Character
                 Animation",
  school =       "The George Washington University",
  year =         "1999",
  address =      "Washington, DC, USA",
  month =        "15 " # may,
  keywords =     "genetic algorithms, genetic programming, computer
                 animation",
  URL =          "http://www.icg.seas.gwu.edu/Publications/gritzdissert.ps.gz",
  broken =       "http://www.seas.gwu.edu/~graphics/papers/gritzdissert.html",
  URL =          "http://phdtree.org/pdf/25207637-evolutionary-controller-synthesis-for-3-d-character-animation/",
  size =         "113 pages",
  abstract =     "Three dimensional computer animation has become
                 increasingly popular over the past decade. Computer
                 animation now has an important role in entertainment,
                 education, and simulation. For computer animation of
                 characters, the role of the animator has unfortunately
                 stayed similar to that of a stop motion animator,
                 rather than like a film director. Research in computer
                 animation has tried to address this by giving higher
                 levels of control to the animator, but these methods
                 often result in lack of fine control over the animated
                 characters. This is inadequate because fine control is
                 essential to both aesthetics and the ability of the
                 animator to direct a meaningful narrative. This
                 dissertation presents methods of articulated figure
                 motion control which attempt to bridge the gap between
                 high level direction and low level control of subtle
                 motion. These methods define motion in terms of goals
                 and ratings. The agents are dynamically-controlled
                 robots whose behavior is determined by robotic
                 controller programs. The controller programs for the
                 robots are evaluated at each time step to yield torque
                 values which drive the dynamic simulation of the
                 motion. We use the AI technique of Genetic Programming
                 (GP) to automatically derive control programs for the
                 agents which achieve the goals. This type of motion
                 specification is an alternative to key framing which
                 allows a highly automated, learning-based approach to
                 generation of motion. This method of motion control is
                 very general (it can be applied to any type of motion),
                 yet it allows for specifications of the types of
                 specific motion which are desired for a high quality
                 animation. We show that complex, specific, physically
                 plausible, and aesthetically appealing motion can be
                 generated using these methods. Both skill-based and
                 action-based motion can be specified in this manner. We
                 also introduce the new paradigm of key marks, a
                 generalization of key framing which is not subject to
                 many of the limitations of key framing.",
}

Genetic Programming entries for Larry Gritz

Citations