  We present a novel autoregression network to generate virtual agents that
convey various emotions through their walking styles or gaits. Given the 3D
pose sequences of a gait, our network extracts pertinent movement features and
affective features from the gait. We use these features to synthesize
subsequent gaits such that the virtual agents can express and transition
between emotions represented as combinations of happy, sad, angry, and neutral.
We incorporate multiple regularizations in the training of our network to
simultaneously enforce plausible movements and noticeable emotions on the
virtual agents. We also integrate our approach with an AR environment using a
Microsoft HoloLens and can generate emotive gaits at interactive rates to
increase the social presence. We evaluate how human observers perceive both the
naturalness and the emotions from the generated gaits of the virtual agents in
a web-based study. Our results indicate around 89% of the users found the
naturalness of the gaits satisfactory on a five-point Likert scale, and the
emotions they perceived from the virtual agents are statistically similar to
the intended emotions of the virtual agents. We also use our network to augment
existing gait datasets with emotive gaits and will release this augmented
dataset for future research in emotion prediction and emotive gait synthesis.
Our project website is available at https://gamma.umd.edu/gen_emotive_gaits/.
