Abstract
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.
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Zhao, R., Szafron, D. (2012). Generating Believable Virtual Characters Using Behavior Capture and Hidden Markov Models. In: van den Herik, H.J., Plaat, A. (eds) Advances in Computer Games. ACG 2011. Lecture Notes in Computer Science, vol 7168. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31866-5_29
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DOI: https://doi.org/10.1007/978-3-642-31866-5_29
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