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Realistic and directable humanlike characters are an ongoing goal in animation. Motion graph data structures hold much promise for achieving this goal. However, the quality of the results obtained from a motion graph may not be easy to predict from the input motion segments. This paper introduces the idea of assessing a data structure such as a motion graph for its utility in a particular application. We focus on navigation tasks and define metrics for evaluating expected path quality and coverage for a given environment. One key to evaluating a motion graph for navigation tasks is to first embed it into the environment in a way that captures all possible paths that might result from 'playing back' the motion graph within that environment. This paper describes an algorithm for accomplishing this embedding that preserves the flexibility of the original motion graph. We use the metrics defined in this paper to compare motion datasets and to highlight areas where these datasets could be improved.
Reitsma et al. (Thu,) studied this question.