How the OCEAN Personality Model Affects the Perception of Crowds

Cataloged from PDF version of article.A personality model named High-Density Autonomous Crowds (HiDAC) simulation system provides individual differences by assigning each person different psychological and physiological traits. Users normally set these parameters to model a crowd's nonuniformity and diversity. The approach creates plausible variations in the crowd and enables novice users to dictate these variations by combining a standard personality model with a high-density crowd simulation. HiDAC addresses the simulation of local behaviors and the global wayfinding of crowds in a dynamically changing environment. It directs autonomous agents' behavior by combining geometric and psychological rules. HiDAC handles collisions through avoidance and response forces. Over long distances, the system applies collision avoidance so that agents can steer around obstacles. HiDAC assigns people specific behaviors. The number of actions they complete depends on their curiosity

Authoring Multi-Actor Behaviors in Crowds With Diverse Personalities

Multi-actor simulation is critical to cinematic content creation, disaster and security simulation, and interactive entertainment. A key challenge is providing an appropriate interface for authoring high-fidelity virtual actors with featurerich control mechanisms capable of complex interactions with the environment and other actors. In this chapter, we present work that addresses the problem of behavior authoring at three levels: Individual and group interactions are conducted in an event-centric manner using parameterized behavior trees, social crowd dynamics are captured using the OCEAN personality model, and a centralized automated planner is used to enforce global narrative constraints on the scale of the entire simulation. We demonstrate the benefits and limitations of each of these approaches and propose the need for a single unifying construct capable of authoring functional, purposeful, autonomous actors which conform to a global narrative in an interactive simulation

Integrating Personality And Emotion For Human Crowd Simulation

Existing research attempts to create realistic crowd simulations by incorporating personality and emotion into intelligent agents. However, personality and emotion were considered separately in existing studies, where the interactions of them are ignored. The main objective of this paper is to propose and implement a framework for crowd simulation with integration of the impacts and interactions of personality and emotion. An interactive solution based on the proposed framework is also developed for visualizing the crowd navigation behavior and collecting the related trajectory data. Three simulated scenarios: pass through, narrow passage, and emergence situation are used to validate the framework and compare the results with recent studies

Placing large group relations into pedestrian dynamics: psychological crowds in counterflow

Understanding influences on pedestrian movement is important to accurately simulate crowd behaviour, yet little research has explored the psychological factors that influence interactions between large groups in counterflow scenarios. Research from social psychology has demonstrated that social identities can influence the micro-level pedestrian movement of a psychological crowd, yet this has not been extended to explore behaviour when two large psychological groups are co-present. This study investigates how the presence of large groups with different social identities can affect pedestrian behaviour when walking in counterflow. Participants (N = 54) were divided into two groups and primed to have identities as either ‘team A’ or ‘team B’. The trajectories of all participants were tracked to compare the movement of team A when walking alone to when walking in counterflow with team B, based on their i) speed of movement and distance walked, and ii) proximity between participants. In comparison to walking alone, the presence of another group influenced team A to collectively self-organise to reduce their speed and distance walked in order to walk closely together with ingroup members. We discuss the importance of incorporating social identities into pedestrian group dynamics for empirically validated simulations of counterflow scenarios

A data-driven approach towards a realistic and generic crowd simulation framework

Jacob Sinclair studied and developed a data-driven approach towards a realistic and generic crowd simulation framework. He found that by using virtual reality and questionnaires, we can gather all types of real world data. He also found that an AI framework developed using all types of data can produce similar results to the real world. This AI framework has the potential to be used to improve areas such as emergency management and response, traffic control, building design, video games, etc

3D Face Synthesis Driven by Personality Impression

Synthesizing 3D faces that give certain personality impressions is commonly needed in computer games, animations, and virtual world applications for producing realistic virtual characters. In this paper, we propose a novel approach to synthesize 3D faces based on personality impression for creating virtual characters. Our approach consists of two major steps. In the first step, we train classifiers using deep convolutional neural networks on a dataset of images with personality impression annotations, which are capable of predicting the personality impression of a face. In the second step, given a 3D face and a desired personality impression type as user inputs, our approach optimizes the facial details against the trained classifiers, so as to synthesize a face which gives the desired personality impression. We demonstrate our approach for synthesizing 3D faces giving desired personality impressions on a variety of 3D face models. Perceptual studies show that the perceived personality impressions of the synthesized faces agree with the target personality impressions specified for synthesizing the faces. Please refer to the supplementary materials for all results.Comment: 8pages;6 figure

Populating virtual cities with diverse physiology driven crowds of intelligent agents

When conducting archaeological excavations of ancient cities, 3D reconstruction has become an important mechanism of documenting the findings and showing the results to general public in an accessible way. Most such reconstructions, however, mainly focus on visualising buildings and artefacts, while rarely simulating the actual people that populated the reconstructed city and aspects of their everyday life. Simulating such people and their lives in all their diversity is a costly and time-consuming exercise comparable in cost and efforts to development of a commercial video game, involving years of development and millions of dollars in funding. In this paper we present a novel approach that can significantly decrease the cost and effort required for simulating everyday life of ancient inhabitants of virtual cities, while still capturing enough detail to be useful in historical simulations. We show how it is possible to manually design a small number of individual avatars and then automatically generate a substantially large crowd of virtual agents, which will live their lives in the simulated city, perform choirs and rituals as well as other routine activities that are consistent with their social status. The key novelty of our approach that enables simulating such sophisticated crowds is the combination of physiological needs - for generating agent goals, emotions and personality - for choosing how to fulfil each goal and genetically informed propagation of appearance and personality traits - to propagate aspects of appearance and behaviour from a small sample of manually designed individuals to large agent groups of a desired size. The usefulness of our approach is demonstrated by applying it to simulating everyday life in the ancient city of Uruk, 3000 B.C.