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    This study introduces a novel algorithm for creating virtual character gaits with distinct dominance traits. The method accurately classifies and generates walking behaviors, enhancing virtual character interactions and relationships.

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    Area of Science:

    • Computer graphics and human-computer interaction.
    • Behavioral psychology and visual perception.
    • Artificial intelligence and machine learning for character animation.

    Background:

    • Virtual characters often lack nuanced behavioral traits like dominance.
    • Existing methods for gait generation do not account for psychological attributes.
    • Quantifying and replicating dominance in virtual character movement is an open challenge.

    Purpose of the Study:

    • To develop a data-driven algorithm for generating and classifying gait dominance in virtual characters.
    • To establish a mapping between observable gait features and perceived dominance levels.
    • To simulate realistic interpersonal dynamics through controlled character movement.

    Main Methods:

    • Conducted a user study to create a data-driven mapping of gaits to dominance labels.
    • Extracted gait features based on established principles from psychology and visual perception.
    • Validated the dominance mapping and classification using a second user study in an immersive virtual environment.
    • Developed a gait dominance classification algorithm achieving approximately 73% accuracy.

    Main Results:

    • Successfully generated walking gaits for virtual characters exhibiting varied dominance traits.
    • Identified key gait features correlating with perceived dominance.
    • Validated the accuracy of the gait dominance classification algorithm.
    • Demonstrated an application simulating interpersonal relationships between virtual characters.

    Conclusions:

    • This research presents the first practical approach for classifying and generating gait dominance in virtual characters.
    • The developed algorithm enhances the realism and expressiveness of virtual character behavior.
    • The findings have implications for more immersive and believable virtual environments and social simulations.