Generating Believable Virtual Characters Using Behavior Capture and Hidden Markov Models
We propose a method of generating natural-looking behaviors for virtual characters using a data-driven method called behavior capture. We describe the techniques for capturing trainer-generated traces, for generalizing these traces, and for using the traces to generate behaviors during game-play. Hidden Markov Models (HMMs) are used as one of the generalization techniques for behavior generation. We compared our proposed method to other existing methods by creating a scene with a set of six variations in a computer game, each using a different method for behavior generation, including our proposed method. We conducted a study in which participants watched the variations and ranked them according to a set of criteria for evaluating behaviors. The study showed that behavior capture is a viable alternative to existing manual scripting methods and that HMMs produced the most highly ranked variation with respect to overall believability.
KeywordsHide Markov Model Hide State Game Designer Sequence Generalization Virtual Character
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