Virtual Crowd Research Articles

Virtual Crowds Rheology: Evaluating the Effect of Character Representation on User Locomotion in Crowds.

Crowd data is a crucial element in the modeling of collective behaviors, and opens the way to simulation for their study or prediction. Given the difficulty of acquiring such data, virtual reality is useful for simplifying experimental processes and opening up new experimental opportunities. This comes at the cost of the need to assess the biases introduced by the use of this technology. Our paper is part of this effort, and investigates the effect of the graphical representation of a crowd on the behavior of a user immersed within. More specifically, we inspect the virtual navigation through virtual crowds, in terms of travel speeds and local navigation choices as a function of the visual representation of the virtual agents that make up the crowd (simple geometric model, anthropomorphic model or realistic model). Through an experiment in which we ask a user to navigate virtual crowds of varying densities, we show that the effect of the visual representation is limited, but that an anthropomorphic representation offers the best trade-off between computational complexity and ecological validity, even though a more realistic representation can be preferred when user behaviour is studied in more details. Our work leads to clear recommendations on the design of immersive simulations for the study of crowd behavior.

“Crowds confined into phalansteries built from their own data, human mass regressed into a hedonistic infant body.”

The article examines how László Garaczi’s Weszteg portrays isolation and mass formation in the context of the coronavirus pandemic. In the first part of my essay, I investigate the representation of the “quarantine state.” I argue that in the novel, technology deepens isolation instead of helping to connect with others. In the next section, I examine how the novel depicts the transformation of our relationship with “live” presence, physical proximity, and mass situations due to the pandemic and increasing technological mediation. As I show, physical contact with others and being in a crowd appear exclusively as inconvenient, frustrating, or threatening experiences in the novel. Thereafter, I analyze the dystopian images of violent, barbaric crowds in the text. I claim that they condense collective fears arising from the pandemic, and the novel sensitively points out the mechanisms of scapegoating in times of crisis. Finally, I argue that the novel also vividly portrays a mass experience that is not connected to physical presence. The “virtual crowd,” organized with the facilitation of mass media, is depicted as a multitude of lonely, isolated individuals. I scrutinize the characteristics of this crowd, as well as the role of biopolitical power and mass media—especially aggressive news streams—in organizing the masses and maintaining isolation.

Experimental Study on the Factors Affecting Pedestrian Exit Selection on the Basis of the Mixed Reality Evacuation LVC Simulation System

Evacuation rules for pedestrians in emergencies are of great significance for the design of building exits, exit management, and evacuation facilities. Based on the mixed reality evacuation LVC simulation system we developed, in this paper, three kinds of pedestrian exit selection experiments were carried out, in which the influence of distance and exit selection on pedestrian exit selection was studied. In line with common sense, during the single-factor control tests, the participants preferred the exit with fewer people and at a closer distance. The two-factor combined effect of the above two factors was also studied. It can be found that the participants preferred to choose the least crowded exit with a closer distance for evacuation. Among these two factors, the participants would give priority to the number of people at the exit. In addition, participants show different trajectories for the difference in the number of people at the two exits. Their walking trajectory was close to an arc line in the case of an equal number of people at two exits. The results of this paper provide a theoretical basis for research on designing evacuation facilities and guiding evacuees, and allows us to explore a new approach for mixed reality evacuation research by conducting virtual crowd experiments in a real environment.

Assessing Human Reactions in a Virtual Crowd Based on Crowd Disposition, Perceived Agency, and User Traits

Immersive virtual environments populated by real and virtual humans provide valuable insights into human decision-making processes under controlled conditions. Existing literature indicates elevated comfort, higher presence, and a more positive user experience when virtual humans exhibit rich behaviors. Based on this knowledge, we conducted a web-based, interactive study, in which participants were embodied within a virtual crowd with complex behaviors driven by an underlying psychological model. While participants interacted with a group of autonomous humanoid agents in a shopping scenario similar to Black Friday, the platform recorded their non-verbal behaviors. In this independent-subjects study, we investigated behavioral and emotional variances across participants with diverse backgrounds focusing on two conditions: perceived agency and the crowd’s emotional disposition. For perceived agency, one group of participants was told that the other crowd members were avatars controlled by humans, whereas another group was told that they were artificial agents. For emotional disposition, the crowd behaved either in a docile or hostile manner. The results suggest that the crowd’s disposition and specific participant traits significantly affected certain emotions and behaviors. For instance, participants collected fewer items and reported a higher increase of negative emotions when placed in a hostile crowd. However, perceived agency did not yield any statistically significant effects.

Enhancing healthcare environment design evaluations through an interactive virtual reality-based approach: A design science research

Ignoring a large number of occupants and their behavior in the Healthcare Environment Design Evaluations (HEDE) may lead to biased evaluation results and inefficiencies in the collaborative design process. This study aims to propose a VR-based approach integrating dynamic virtual crowds and human-environment interactions to improve participants’ spatial perception thereby increasing the effectiveness and efficiency of HEDE. This study followed Design Science Research (DSR) to develop an interactive VR-based workflow which was tested in a controlled experiment on the renovation of a real trauma intensive care unit and evaluated by 14 stakeholders including staff and architects. Results of the evaluation showed that dynamic virtual crowds and human-environment interactions helped participants improve spatial perception and were considered crucial for HEDE. The interactive VR-based approach met the criteria of “satisfaction” in terms of accuracy and efficiency. This study underscores the synergy between advanced technology and human-centered design, contributing to social sustainability.

With or Without You: Effect of Contextual and Responsive Crowds on VR-based Crowd Motion Capture.

While data is vital to better understand and model interactions within human crowds, capturing real crowd motions is extremely challenging. Virtual Reality (VR) demonstrated its potential to help, by immersing users into either simulated virtual crowds based on autonomous agents, or within motion-capture-based crowds. In the latter case, users' own captured motion can be used to progressively extend the size of the crowd, a paradigm called Record-and-Replay (2R). However, both approaches demonstrated several limitations which impact the quality of the acquired crowd data. In this paper, we propose the new concept of contextual crowds to leverage both crowd simulation and the 2R paradigm towards more consistent crowd data. We evaluate two different strategies to implement it, namely a Replace-Record-Replay (3R) paradigm where users are initially immersed into a simulated crowd whose agents are successively replaced by the user's captured-data, and a Replace-Record-Replay-Responsive (4R) paradigm where the pre-recorded agents are additionally endowed with responsive capabilities. These two paradigms are evaluated through two real-world-based scenarios replicated in VR. Our results suggest that the behaviors observed in VR users with surrounding agents from the beginning of the recording process are made much more natural, enabling 3R or 4R paradigms to improve the consistency of captured crowd datasets.

Haptic feedback in a virtual crowd scenario improves the emotional response

Research has shown that incorporating haptics into virtual environments can increase sensory fidelity and provide powerful and immersive experiences. However, current studies on haptics in virtual interactions primarily focus on one-on-one scenarios, while kinesthetic haptic interactions in large virtual gatherings are underexplored. This study aims to investigate the impact of kinesthetic haptics on eliciting emotional responses within crowded virtual reality (VR) scenarios. Specifically, we examine the influence of type or quality of the haptic feedback on the perception of positive and negative emotions. We designed and developed different combinations of tactile and torque feedback devices and evaluated their effects on emotional responses. To achieve this, we explored different combinations of haptic feedback devices, including “No Haptic,” “Tactile Stimulus” delivering tactile cues, and “Haptic Stimulus” delivering tactile and torque cues, in combination with two immersive 360-degree video crowd scenarios, namely, “Casual Crowd” and “Aggressive Crowd.” The results suggest that varying the type or quality of haptic feedback can evoke different emotional responses in crowded VR scenarios. Participants reported increased levels of nervousness with Haptic Stimulus in both virtual scenarios, while both Tactile Stimulus and Haptic Stimulus were negatively associated with pleasantness and comfort during the interaction. Additionally, we observed that participants’ sense of touch being real was enhanced in Haptic Stimulus compared to Tactile Stimulus. The “Haptic Stimulus” condition had the most positive influence on participants’ sense of identification with the crowd.

Is the neighborhood of interaction in human crowds metric, topological, or visual?

Global patterns of collective motion in bird flocks, fish schools, and human crowds are thought to emerge from local interactions within a neighborhood of interaction, the zone in which an individual is influenced by their neighbors. Both metric and topological neighborhoods have been reported in animal groups, but this question has not been addressed for human crowds. The answer has important implications for modeling crowd behavior and predicting crowd disasters such as jams, crushes, and stampedes. In a metric neighborhood, an individual is influenced by all neighbors within a fixed radius, whereas in a topological neighborhood, an individual is influenced by a fixed number of nearest neighbors, regardless of their physical distance. A recently proposed alternative is a visual neighborhood, in which an individual is influenced by the optical motions of all visible neighbors. We test these hypotheses experimentally by asking participants to walk in real and virtual crowds and manipulating the crowd's density. Our results rule out a topological neighborhood, are approximated by a metric neighborhood, but are best explained by a visual neighborhood that has elements of both. We conclude that the neighborhood of interaction in human crowds follows naturally from the laws of optics and suggest that previously observed "topological" and "metric" interactions might be a consequence of the visual neighborhood.

Human‐virtual crowd interaction: Towards understanding the effects of crowd avoidance proximity in an immersive virtual environment

SummaryThis paper focuses on understanding how study participants interact and perceive a virtual crowd in an immersive virtual environment. Specifically, our within‐group exploratory study investigated how avoidance proximity variations (i.e., low, medium, and high avoidance proximity [defined as avoidance radius]) assigned to crowd agents impacted participants' interaction with the virtual crowd. During the study, we instructed our participants to walk in a virtual environment. At the same time, we had a virtual crowd scripted to walk toward the start position of the participant following a straight path. During the participants' walking task, we collected movement data (i.e., trajectory length and completion time) and immediately after each experimental condition, we asked participants to self‐report their experience (i.e., co‐presence, behavioral independence, crowd realism, crowd interaction realism, perceived politeness, and emotional reactivity). Based on the collected data, we found that when we exposed our participants to the high avoidance proximity condition, they: (1) followed longer paths, (2) spent more time reaching the target goal, (3) rated the virtual crowd less polite, (4) rated the virtual crowd and their interaction with the virtual crowd less realistic, (5) rated the behavior independence of the virtual crowd lower, and (6) self‐reported higher emotional reactivity. We discuss our findings and suggestions for further research on human‐virtual crowd interaction.

Does attention influence who you follow in a crowd? Tracking neighbors vs. following your friends

Does attention increase the influence of neighbors in a crowd? Rio, Dachner, & Warren’s (2018) model assumes ensemble perception of crowd motion, and yet it seems that we can follow our friends and ignore others. We adapted the Multiple Object Tracking paradigm to test whether selective attention to a subset of the crowd spontaneously influences following behavior. In Experiment 1, the participant walked with a virtual crowd 9 neighbors, viewed in an HMD. At the beginning of each trial, a target set of 2-6 neighbors, or all 9 neighbors (control), blinked on and off; participants were instructed to attend to and track those neighbors as they walked with the crowd. The heading (walking direction) of targets or distractors was perturbed (±20°) midway through the trial. As a tracking check, a randomized memory probe appeared at the end of the trial. Participant heading, speed, facing direction (yaw angle) and probe responses were recorded. A linear mixed effects regression revealed that participant heading was influenced by the number of turning neighbors (χ2(1) = 17.19, p < 0.001), but not by attention to targets. Rather, participant heading was statistically equivalent to the average heading direction of the crowd (BF01 = 11.90), consistent with ensemble perception. Probe responses were highly accurate, indicating that participants successfully tracked targets. In Experiment 2, we tested whether participants can follow a group of “friends” when explicitly instructed to do so. Participants were first trained to recognize a set of avatar friends, which replaced blinking targets; otherwise the design was the same. In this case, participants fully segmented friends from a crowd of strangers and exclusively followed them (χ2(1) = 59.27, p<0.001), breaking ensemble perception. Subsequent experiments are investigating whether this “friend” effect is due to task-relevant attention (explicit instructions to follow targets) or grouping by familiarity.

Understanding reinforcement learned crowds

Simulating trajectories of virtual crowds is a commonly encountered task in Computer Graphics. Several recent works have applied Reinforcement Learning methods to animate virtual agents, however they often make different design choices when it comes to the fundamental simulation setup. Each of these choices comes with a reasonable justification for its use, so it is not obvious what is their real impact, and how they affect the results. In this work, we analyze some of these arbitrary choices in terms of their impact on the learning performance, as well as the quality of the resulting simulation measured in terms of the energy efficiency. We perform a theoretical analysis of the properties of the reward function design, and empirically evaluate the impact of using certain observation and action spaces on a variety of scenarios, with the reward function and energy usage as metrics. We show that directly using the neighboring agents’ information as observation generally outperforms the more widely used raycasting. Similarly, using nonholonomic controls with egocentric observations tends to produce more efficient behaviors than holonomic controls with absolute observations. Each of these choices has a significant, and potentially nontrivial impact on the results, and so researchers should be mindful about choosing and reporting them in their work.

Affective experience in a virtual crowd regulates perceived travel time.

The online version contains supplementary material available at 10.1007/s10055-022-00713-8.

Creating Interactive Crowds with Reinforcement Learning

The entertainment industry, as well as the field of Computer Graphics, frequently faces the issue of creating large virtual crowds that would populate a scene. One of the ways to achieve that, particularly with modern rendering techniques, is by using simulation -- this, however, is nontrivial to design and control. The main goal of my PhD work is working towards the creation of a tool enabling the creation of virtual crowds that one can interact with, and we believe the best way to that is through Multiagent Reinforcement Learning techniques. These animated crowds can then be used both in movies and video games. Especially for the latter, it is highly desirable that both the crowd as a whole, as well as the individual characters, can react to the user's input in real time.

Crowd navigation in an unknown and complex environment based on deep reinforcement learning

AbstractWe propose a virtual crowd navigation approach based on deep reinforcement learning to improve the adaptability of virtual crowds in an unknown and complex environment. To address the problem of local optimum or slow iteration or even failure to converge due to sparse rewards in complex environments, we integrate the curiosity‐driven mechanism, the key navigation points acquisition and the failure path penalty method in addition to combining long short‐term memory networks, dynamic obstacle collision prediction with proximal policy optimization algorithms, which realizes the crowd navigation in a complex environment. The experimental results show that the proposed approach can simulate the motions of virtual crowds in various dynamic and complex scenarios without the environment modeling. The use of continuous action space also ensures that the movement trajectories of the virtual crowds are more realistic and natural. Furthermore, our approach can provide analysis and demonstration tools for a variety of independent collaborations such as competition and cooperation of group intelligence in an open and dynamic environment.

A Survey on Reinforcement Learning Methods in Character Animation

AbstractReinforcement Learning is an area of Machine Learning focused on how agents can be trained to make sequential decisions, and achieve a particular goal within an arbitrary environment. While learning, they repeatedly take actions based on their observation of the environment, and receive appropriate rewards which define the objective. This experience is then used to progressively improve the policy controlling the agent's behavior, typically represented by a neural network. This trained module can then be reused for similar problems, which makes this approach promising for the animation of autonomous, yet reactive characters in simulators, video games or virtual reality environments. This paper surveys the modern Deep Reinforcement Learning methods and discusses their possible applications in Character Animation, from skeletal control of a single, physically‐based character to navigation controllers for individual agents and virtual crowds. It also describes the practical side of training DRL systems, comparing the different frameworks available to build such agents.

The One-Man-Crowd: Single User Generation of Crowd Motions Using Virtual Reality.

Crowd motion data is fundamental for understanding and simulating realistic crowd behaviours. Such data is usually collected through controlled experiments to ensure that both desired individual interactions and collective behaviours can be observed. It is however scarce, due to ethical concerns and logistical difficulties involved in its gathering, and only covers a few typical crowd scenarios. In this work, we propose and evaluate a novel Virtual Reality based approach lifting the limitations of real-world experiments for the acquisition of crowd motion data. Our approach immerses a single user in virtual scenarios where he/she successively acts each crowd member. By recording the past trajectories and body movements of the user, and displaying them on virtual characters, the user progressively builds the overall crowd behaviour by him/herself. We validate the feasibility of our approach by replicating three real experiments, and compare both the resulting emergent phenomena and the individual interactions to existing real datasets. Our results suggest that realistic collective behaviours can naturally emerge from virtual crowd data generated using our approach, even though the variety in behaviours is lower than in real situations. These results provide valuable insights to the building of virtual crowd experiences, and reveal key directions for further improvements.

Authoring Virtual Crowds: A Survey

AbstractRecent advancements in crowd simulation unravel a wide range of functionalities for virtual agents, delivering highly‐realistic, natural virtual crowds. Such systems are of particular importance to a variety of applications in fields such as: entertainment (e.g., movies, computer games); architectural and urban planning; and simulations for sports and training. However, providing their capabilities to untrained users necessitates the development of authoring frameworks. Authoring virtual crowds is a complex and multi‐level task, varying from assuming control and assisting users to realise their creative intents, to delivering intuitive and easy to use interfaces, facilitating such control. In this paper, we present a categorisation of the authorable crowd simulation components, ranging from high‐level behaviours and path‐planning to local movements, as well as animation and visualisation. We provide a review of the most relevant methods in each area, emphasising the amount and nature of influence that the users have over the final result. Moreover, we discuss the currently available authoring tools (e.g., graphical user interfaces, drag‐and‐drop), identifying the trends of early and recent work. Finally, we suggest promising directions for future research that mainly stem from the rise of learning‐based methods, and the need for a unified authoring framework.

The visual coupling between neighbours explains local interactions underlying human 'flocking'.

Patterns of collective motion in bird flocks, fish schools and human crowds are believed to emerge from local interactions between individuals. Most ‘flocking' models attribute these local interactions to hypothetical rules or metaphorical forces and assume an omniscient third-person view of the positions and velocities of all individuals in space. We develop a visual model of collective motion in human crowds based on the visual coupling that governs pedestrian interactions from a first-person embedded viewpoint. Specifically, humans control their walking speed and direction by cancelling the average angular velocity and optical expansion/contraction of their neighbours, weighted by visibility (1 − occlusion). We test the model by simulating data from experiments with virtual crowds and real human ‘swarms'. The visual model outperforms our previous omniscient model and explains basic properties of interaction: ‘repulsion' forces reduce to cancelling optical expansion, ‘attraction' forces to cancelling optical contraction and ‘alignment' to cancelling the combination of expansion/contraction and angular velocity. Moreover, the neighbourhood of interaction follows from Euclid's Law of perspective and the geometry of occlusion. We conclude that the local interactions underlying human flocking are a natural consequence of the laws of optics. Similar perceptual principles may apply to collective motion in other species.

Data-driven Crowd Modeling Techniques: A Survey

Data-driven crowd modeling has now become a popular and effective approach for generating realistic crowd simulation and has been applied to a range of applications, such as anomaly detection and game design. In the past decades, a number of data-driven crowd modeling techniques have been proposed, providing many options for people to generate virtual crowd simulation. This article provides a comprehensive survey of these state-of-the-art data-driven modeling techniques. We first describe the commonly used datasets for crowd modeling. Then, we categorize and discuss the state-of-the-art data-driven crowd modeling methods. After that, data-driven crowd model validation techniques are discussed. Finally, six promising future research topics of data-driven crowd modeling are discussed.

Decision-Making in Human Crowds: Nonlinear Competition Dynamics

When a crowd splits into two groups walking in different directions, how does an individual decide which group to follow? We previously found (VSS 2019) that participants average the heading directions of all neighbors in the field of view up to an angular difference of 40° between groups, consistent with Rio, Dachner & Warren’s (PRSB 2018) neighborhood model. Here we investigate what rules govern individual decisions with larger angular differences (≥80°).Participants (N=10) were instructed to “walk with” a virtual crowd (8 or 16 virtual humans) while wearing a Samsung Odyssey HMD (110º FOV). After 2-3s, one group made a small turn (20° left or right) and the other group made a large turn (60°, 100°, 140°, or 180°) in the other direction. We manipulated the percentage of the crowd making the large turn (0%, 25%, 50%, 75%, 100%), and measured the participant’s walking direction. The results show a strong attraction to follow the majority (p<0.001), with no influence of total crowd size (p=0.27, contrary to a quorum). However, this effect trades off with an attraction to the smaller (20°) turn: as the magnitude of the large turn increased, participants were more likely to follow a minority making the smaller, less effortful turn (p<0.001). Interestingly, there were individual differences: while most participants followed this trade-off, a few always preferred either the majority or the small turn. To model this data, we introduced nonlinear competition dynamics into our neighborhood model, such that an individual decision is driven by the relative weight of the crowd proportion and the angular difference between groups. We fit the competition parameters to each participant, and simulated heading in all experimental trials. The new model accounted for 86% of the individual decisions in the data set.