Perception of Personality Traits in Crowds of Virtual Humans Lucas Nardino

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Perception of Personality Traits in Crowds of

Virtual Humans

Lucas Nardino

Virtual Humans Lab

School of Technology

Pontiﬁcal Catholic University

of Rio Grande do Sul

Porto Alegre, Brazil

lucas.nardino@edu.pucrs.br

Enzo Krzmienszki

Virtual Humans Lab

School of Technology

Pontiﬁcal Catholic University

of Rio Grande do Sul

Porto Alegre, Brazil

enzo.neves@edu.pucrs.br

Vin´

ıcius Jurinic Cassol

Virtual Humans Lab

School of Technology

Pontiﬁcal Catholic University

of Rio Grande do Sul

Porto Alegre, Brazil

vinicius.cassol@edu.pucrs.br

Diogo Schaffer

Virtual Humans Lab

School of Technology

Pontiﬁcal Catholic University

of Rio Grande do Sul

Porto Alegre, Brazil

diogo.schaffer@acad.pucrs.br

Victor Fl´

avio de Andrade Araujo

Virtual Humans Lab

School of Technology

Pontiﬁcal Catholic University

of Rio Grande do Sul

Porto Alegre, Brazil

victor.ﬂavio@acad.pucrs.br

Rodolfo Migon Favaretto

Virtual Humans Lab

School of Technology

Pontiﬁcal Catholic University

of Rio Grande do Sul

Porto Alegre, Brazil

rodolfo.favaretto@gmail.com

Felipe Elsner

Virtual Humans Lab

School of Technology

Pontiﬁcal Catholic University

of Rio Grande do Sul

Porto Alegre, Brazil

f.elsner@edu.pucrs.br

Gabriel Fonseca Silva

Virtual Humans Lab

School of Technology

Pontiﬁcal Catholic University

of Rio Grande do Sul

Porto Alegre, Brazil

gabriel.fonseca94@edu.pucrs.br

Soraia Raupp Musse

Virtual Humans Lab

School of Technology

Pontiﬁcal Catholic University

of Rio Grande do Sul

Porto Alegre, Brazil

soraia.musse@pucrs.br

Abstract—This paper proposes a perceptual visual analysis

regarding the personality of virtual humans. Many studies have

presented ﬁndings regarding the way human beings perceive

virtual humans with respect to their faces, body animation,

motion in the virtual environment and etc. We are interested

in investigating the way people perceive visual manifestations of

virtual humans’ personality traits when they are interactive and

organized in groups. Many applications in games and movies can

beneﬁt from the ﬁndings regarding the perceptual analysis with

the main goal to provide more realistic characters and improve

the users’ experience. We provide experiments with subjects and

obtained results indicate that, although is very subtle, people

perceive more the extraversion (the personality trait that we

measured), into the crowds of virtual humans, when interacting

with virtual humans behaviors, than when just observing as a

spectator camera.

Index Terms—crowd simulation, virtual agents, perception,

personality traits.

I. INTRODUCTION

1Since the pioneer work proposed by Thalmann and

Musse [1], many other methods were proposed for crowd

simulation, each one with a signiﬁcant contribution. There are

methods that deal with crowds from a microscopic point of

1Draft version made for arXiv: https://arxiv.org/

view [2], [3], as well methods that deal with a macroscopic

point of view [4], [5], and, even, methods that combine both

microscopic and macroscopic simulation strategies [6]. Others

explored how to compare crowds [7], high dense crowds [2],

[8], heterogeneous behaviors [9], navigation control [10], and

personality traits for agents [11]–[13].

Despite the great number of methods proposed for the most

varied range of subjects concerning crowd simulation, only

very few of them tackled the problem of perceptual analysis

of behaviors in crowds. Indeed, human perception is essential

for Computer Graphics (CG). Several techniques developed

in the past were based on knowledge of human vision, for

example, the interpretation of visual stimuli [14]. These stimuli

generate information, which is processed and placed in a

speciﬁc context. Human perception is a theme present in

several researches in CG [14], [15], and it is considered very

relevant when discussing the evolution of virtual humans.

Virtual humans can be observed through stimuli such as

images, videos, games, and Virtual Reality interactions, among

others. For these virtual humans do not generate uncomfortable

perceptions and falling into an Uncanny Valley [16], they need

to present characteristics common to human beings, such as

emotions, personality traits, interactions, expressions, etc.

arXiv:2210.03042v1 [cs.HC] 6 Oct 2022

Crowd perception is very important for learning about group

behavior, in which observers can see interpersonal interaction

on a collective level [17]. The area of crowd perception has

grown in recent years in several scientiﬁc researches (both

through psychology and computing), such as perception of

different models of agents in crowd simulations [18], percep-

tion of geometric and cultural features in virtual crowds [19],

[20], perception of density in virtual crowds from two points

of view [21], effects on users during interaction with a

virtual crowd in an immersive virtual reality environment [22],

studies of social categorization and emotions in crowds using

ensemble coding [17], [23], among other researches. However,

these methods do not focus on the perception of interactions

between agents, between agent and user, and the impact of

geometric personalities and emotions (that is, no facial and

body expressions) on the perception of these interactions. In

this work, we deﬁne three hypotheses we want to answer:

•H01deﬁning that people with only observational control

of agents in the crowd (do not interfere with crowd

dynamics) perceive interactions similarly to people with

control of agents in the crowd (the user is considered a

crowd agent);

•H02deﬁning that people with only observational control

of crowd agents perceive different personalities and emo-

tions similarly to people with control of crowd agents.

In this case, as in our work we only use extraversion

personality trait, different personalities mean that an agent

can or cannot be extraverted;

•H03deﬁning that the perception of interactions in crowds

is not related to the perception of different personalities

and emotions;

To try to answer the hypotheses, we created three scenarios

with virtual crowds: i) Scenario 1, in which a user controlled

a ﬁrst-person camera throughout the entire scenario, without

interfering with the agents’ behavior; ii) Scenario 2, in which a

user also controlled a ﬁrst-person camera throughout the entire

scenario, but he/she is considered as one agent of the simulated

crowd, using the BioCrowds [24] model; iii) Scenario 3, in

which a user is also an agent in the crowd, but the simulated

crowd is different from Scenario 2 because we use an exten-

sion of BioCrowds model, called Normal Life behaviors [25],

i.e., people are not in emergent situation. As the contribution

of this paper, we introduced in BioCrowds the Extraversion

factor to be distributed among the agents, so they are impacted

by their levels of extraversion when applying their motion.

Such factor is inspired in the personality traits methods, as

proposed by Durupinar et al. [11]. From the observations

and interactions with the scenarios, people answered questions

about how they perceive the agents’ interactions, and their

different personalities and emotions, as discussed in this paper.

This paper is organized as follows: Section II presents

the related work, while Section III presents the methodology

proposed. Section IV presents the results achieved with our

method and evaluation with subjects. Finally, Section VI

presents the ﬁnal considerations and future work of our

method.

II. RELATED WORK

This section discusses some work related to pedestrian

and crowd behavioral analysis, focusing on personality traits,

emotion, and perception. Knob et al. [26] presented work

related to visualizing interactions between pedestrians in video

sequences and virtual agents in crowd simulations. OCEAN-

based factors gave interactions for each pedestrian and agent.

OCEAN [27], [28] is the most commonly used personality

trait model for this type of analysis, also known as the

Big-Five: O - Openness to experience: “the active seeking

and appreciation of new experiences”; C - Conscientiousness:

“degree of organization, persistence, control, and motivation

in goal directed-behavior”; E - Extraversion: “quantity and

intensity of energy directed outwards in the social world”; A -

Agreeableness: “the kinds of interaction an individual prefers

from compassion to tough-mindedness”; N - Neuroticism:

“how much prone to psychological distress the individual

is” [29]. Durupinar et al. [30] also used OCEAN to visually

represent personality traits.

Visual representation of agents is given in various ways,

for example, the animations of agents are based on these two

cultural features (OCEAN and emotion). If an agent is sad,

the animation will represent that emotion. Yang et al. [21]

conducted a study analyzing perception to determine the

impact of groups at various densities, using two points of view:

top and ﬁrst-person. In addition to that, they examined what

kind of camera position might be best for density perception.

Regarding realism perception, the work proposed by Araujo

et al. [31] investigated people’s perception of characters cre-

ated using CG, comparing if they feel more comfortable with

more recent CG characters or older ones. The authors found

out that the perceived comfort about newer CG characters was

more signiﬁcant than the perception of comfort about older CG

characters. Also, people’s perception of comfort in 2020 was

greater than people’s perception in 2012.

In another work [19], [20], the authors evaluate the hu-

man perception regarding geometric features, personalities,

and emotions in avatars. Results indicate that, even without

explaining to the participants the concepts of cultural features

and how they were calculated (considering the geometric fea-

tures), in most cases, the participants perceived the personality

and emotion expressed by avatars, even without faces and body

expressions.

The work proposed by Volonte et al. [22] examined the

effects on users during interaction with a virtual human crowd

in an immersive virtual reality environment. They found that

the users’ were able to interpret the verbal and non-verbal

behaviors of the virtual human characters where Positive

emotional crowds elicit the highest scores in the variables

related to interaction with the virtual characters.

Next, we present the proposed model to generate virtual

agents with personality traits (in this case, we just used

the extraversion personality trait) and how we evaluate the

people’s perception.

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PerceptionofPersonalityTraitsinCrowdsofVirtualHumansLucasNardinoVirtualHumansLabSchoolofTechnologyPonticalCatholicUniversityofRioGrandedoSulPortoAlegre,Brazillucas.nardino@edu.pucrs.brEnzoKrzmienszkiVirtualHumansLabSchoolofTechnologyPonticalCatholicUniversityofRioGrandedoSulPortoAlegre,Brazilenzo....

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