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

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

@article{DBLP:journals/corr/abs-1901-02037,
  author    = {Tanmay Randhavane and
               Aniket Bera and
               Emily Kubin and
               Kurt Gray and
               Dinesh Manocha},
  title     = {Modeling Data-Driven Dominance Traits for Virtual Characters using
               Gait Analysis},
  journal   = {CoRR},
  volume    = {abs/1901.02037},
  year      = {2019},
  url       = {http://arxiv.org/abs/1901.02037},
  archivePrefix = {arXiv},
  eprint    = {1901.02037},
  timestamp = {Thu, 31 Jan 2019 13:52:49 +0100},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1901-02037},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}