Modelling selective visual attention for autonomous virtual characters

Kokkinara, Elena; Oyekoya, Oyewole; Steed, Anthony

doi:10.1002/cav.425

Cited by 10 publications

(5 citation statements)

References 18 publications

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“…In other works, gaze has been modelled in face-to-face scenarios for more personal situations [Bailly et al 2010]. Gaze has also been modelled in interactive environments with real users [Kokkinara et al 2011;. One approach [Grillon and Thalmann 2009] attempted to simulate gaze in crowds for aesthetics, although it didn't alter the trajectories of agents or investigate the effects of distraction on crowds.…”

Section: Gaze and Attention Modellingmentioning

confidence: 99%

Watch Out! Modelling Pedestrians with Egocentric Distractions

Kremer

Haworth

Kapadia

et al. 2020

Motion, Interaction and Games

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The use of mobile devices is one of the most commonly observed family of distracted behaviours exhibited by pedestrians in urban environments. We develop an event-driven behaviour tree model for distracted pedestrians that includes initiating mobile device use as well as terminating or pausing mobile device use based on internal or external cues to refocus attention. We present a simple, probabilistic attention model for such pedestrians. The proposed model is not meant to be complete. It primarily focuses on computing the probability that a distracted agent looks up, based on the agent's individual characteristics and the elements in their environment. We condition the potentially attention grabbing elements in the environment on distraction-specific egocentric fields for visual attention. We also propose an oriented ellipse model for capturing the affects of cognitively fuzzy goals during distracted navigation. Our model is simple and intuitively parameterized, and thus can be easily edited and extended. CCS CONCEPTS • Computing methodologies → Agent / discrete models; Procedural animation.

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Section: Gaze and Attention Modellingmentioning

confidence: 99%

Watch Out! Modelling Pedestrians with Egocentric Distractions

Kremer

Haworth

Kapadia

et al. 2020

Motion, Interaction and Games

View full text Add to dashboard Cite

show abstract

“…Kokkinara et al implemented a model that simulates gaze interaction between the agents in crowds, which can be autonomous virtual agents or user‐controlled virtual agents. However, being in a crowd as individuals or belonging to groups in the crowd has different psychological effects that can alter behaviors of characters, which are missing in this model.…”

Section: Related Workmentioning

confidence: 99%

A group‐based approach for gaze behavior of virtual crowds incorporating personalities

Ağıl

Güdükbay

2018

Computer Animation & Virtual

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Predicting interest points of virtual characters and accurately simulating their gaze behavior play a significant role for realistic crowd simulations. We propose a saliency model that enables virtual agents to produce plausible gaze behavior. The model measures the effects of distinct saliency features implemented by examining the state‐of‐the‐art perception studies. When predicting an agent's interest point, we compute the saliency scores by using a weighted sum function for other agents and environment objects in the field of view of the agent for each frame. Then, we determine the most salient entity for each agent in the scene; thus, agents gain a visual understanding of their environment. Besides, our model introduces new aspects to crowd perception, such as perceiving characters as groups of people and applying social norms on crowd gaze behavior, effects of agent personality on gaze, gaze copy phenomena, and effects of agent velocity on attention. For evaluation, we compare the resulting saliency gaze model with real‐world crowd behavior in captured videos. In the experiments, we simulate the gaze behavior in real crowds. The results show that the proposed approach generates plausible gaze behaviors and is easily adaptable to varying scenarios for virtual crowds.

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“…In other words, higher attention is given to the person who is trying to engage in a reciprocal social interaction. Zaraki et al, Kokkinara et al, Xu et al, and Grillon et al, developed attention models for virtual humans and robots using multiple features such as proximity and velocity.…”

Section: Related Workmentioning

confidence: 99%

Autonomous social gaze model for an interactive virtual character in real‐life settings

Yumak

Brink

Egges

2017

Computer Animation & Virtual

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This paper presents a gaze behavior model for an interactive virtual character situated in the real world. We are interested in estimating which user has an intention to interact, in other words which user is engaged with the virtual character. The model takes into account behavioral cues such as proximity, velocity, posture, and sound; estimates an engagement score; and drives the gaze behavior of the virtual character.Initially, we assign equal weights to these features. Using data collected in a real setting, we analyze which features have higher importance. We found that the model with weighted features correlates better with the ground-truth data.

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Modelling selective visual attention for autonomous virtual characters

Cited by 10 publications

References 18 publications

Watch Out! Modelling Pedestrians with Egocentric Distractions

Watch Out! Modelling Pedestrians with Egocentric Distractions

A group‐based approach for gaze behavior of virtual crowds incorporating personalities

Autonomous social gaze model for an interactive virtual character in real‐life settings

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