3 research outputs found
Data-Driven Modeling of Group Entitativity in Virtual Environments
We present a data-driven algorithm to model and predict the socio-emotional
impact of groups on observers. Psychological research finds that highly
entitative i.e. cohesive and uniform groups induce threat and unease in
observers. Our algorithm models realistic trajectory-level behaviors to
classify and map the motion-based entitativity of crowds. This mapping is based
on a statistical scheme that dynamically learns pedestrian behavior and
computes the resultant entitativity induced emotion through group motion
characteristics. We also present a novel interactive multi-agent simulation
algorithm to model entitative groups and conduct a VR user study to validate
the socio-emotional predictive power of our algorithm. We further show that
model-generated high-entitativity groups do induce more negative emotions than
low-entitative groups.Comment: Accepted at VRST 2018, November 28-December 1, 2018, Tokyo, Japa
EVA: Generating Emotional Behavior of Virtual Agents using Expressive Features of Gait and Gaze
We present a novel, real-time algorithm, EVA, for generating virtual agents
with various perceived emotions. Our approach is based on using Expressive
Features of gaze and gait to convey emotions corresponding to happy, sad,
angry, or neutral. We precompute a data-driven mapping between gaits and their
perceived emotions. EVA uses this gait emotion association at runtime to
generate appropriate walking styles in terms of gaits and gaze. Using the EVA
algorithm, we can simulate gaits and gazing behaviors of hundreds of virtual
agents in real-time with known emotional characteristics. We have evaluated the
benefits in different multi-agent VR simulation environments. Our studies
suggest that the use of expressive features corresponding to gait and gaze can
considerably increase the sense of presence in scenarios with multiple virtual
agents.Comment: In Proceedings of ACM Symposium on Applied Perception 201
FVA: Modeling Perceived Friendliness of Virtual Agents Using Movement Characteristics
We present a new approach for improving the friendliness and warmth of a
virtual agent in an AR environment by generating appropriate movement
characteristics. Our algorithm is based on a novel data-driven friendliness
model that is computed using a user-study and psychological characteristics. We
use our model to control the movements corresponding to the gaits, gestures,
and gazing of friendly virtual agents (FVAs) as they interact with the user's
avatar and other agents in the environment. We have integrated FVA agents with
an AR environment using with a Microsoft HoloLens. Our algorithm can generate
plausible movements at interactive rates to increase the social presence. We
also investigate the perception of a user in an AR setting and observe that an
FVA has a statistically significant improvement in terms of the perceived
friendliness and social presence of a user compared to an agent without the
friendliness modeling. We observe an increment of 5.71% in the mean responses
to a friendliness measure and an improvement of 4.03% in the mean responses to
a social presence measure.Comment: To appear in ISMAR 2019 Special Issue of TVC