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We present a data-driven algorithm for generating gaits of virtual characterswith varying dominance traits. Our formulation utilizes a user study toestablish a data-driven dominance mapping between gaits and dominance labels.We use our dominance mapping to generate walking gaits for virtual charactersthat exhibit a variety of dominance traits while interacting with the user.Furthermore, we extract gait features based on known criteria in visualperception and psychology literature that can be used to identify the dominancelevels of any walking gait. We validate our mapping and the perceived dominancetraits by a second user study in an immersive virtual environment. Our gaitdominance classification algorithm can classify the dominance traits of gaitswith ~73% accuracy. We also present an application of our approach thatsimulates interpersonal relationships between virtual characters. To the bestof our knowledge, ours is the first practical approach to classifying gaitdominance and generate dominance traits in virtual characters.

Modeling Data-Driven Dominance Traits for Virtual Characters using Gait Analysis