The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

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 obtainable from a motion graph may not be easy to predict from its input motion clips. This paper describes a method for using task-based metrics to evaluate the capability of a motion graph to create the set of animations required by a particular application. We examine this capability for typical motion graphs across a range of tasks and environments. We ﬁnd that motion graph capability degrades rapidly with increases in the complexity of the target environment or required tasks, and that addressing deﬁciencies in a brute-force manner tends to lead to large, unwieldy motion graphs. The results of this method can be used to evaluate the extent to which a motion graph will fulﬁll the requirements of a particular application, lessening the risk of the data structure performing poorly at an inopportune moment. The method can also be used to characterize the deﬁciencies of motion graphs whose performance will not be suﬃcient, and to evaluate the relative eﬀectiveness of diﬀerent options for improving those motion graphs.

Keywords

motion capability, capability metrics, motion capture, human motion, motion graphs, motion graph embedding, editing model

Notes

Text Reference

Paul Reitsma and Nancy Pollard, "Evaluating Motion Graphs for Character Animation," ACM Transactions on Graphics, 26(4), , October, 2007

BibTeX Reference

@article{Reitsma_2007_7287,
   author = "Paul Reitsma and Nancy Pollard",
   title = "Evaluating Motion Graphs for Character Animation",
   journal = "ACM Transactions on Graphics, 26(4)",
   month = "October",
   year = "2007",
   number= "CMU-RI-TR-",
}

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