Human Crowd Simulation: What CanWe Learn From ALife?

2002

Work on levels of detail for human simulation has occurred mainly on a geometrical level, either by reducing the numbers of polygons representing a virtual human, or replacing them with a two-dimensional imposter. Approaches that reduce the complexity of motions generated have also been proposed. In this paper, we describe ongoing development of a framework for Adaptive Level Of Detail for Human Animation (ALOHA), which incorporates levels of detail for not only geometry and motion, but also includes a complexity gradient for natural behaviour, both conversational and social.

2002

Work on levels of detail for human simulation has occurred mainly on a geometrical level, either by reducing the numbers of polygons representing a virtual human, or replacing them with a two-dimensional imposter. Approaches that reduce the complexity of motions generated have also been proposed. In this paper, we describe ongoing development of a framework for Adaptive Level Of Detail for Human Animation (ALOHA), which incorporates levels of detail for not only geometry and motion, but also includes a complexity gradient for natural behaviour, both conversational and social.

IEEE Transactions on Visualization and Computer Graphics, 2001

This paper describes a model for simulating crowds of humans in real time. We deal with a hierarchy composed of virtual crowds, groups and individuals. The groups are the most complex structure that can be controlled in different degrees of autonomy. This autonomy refers to the extent to which the virtual agents are independent of the user intervention and also the amount of information needed to simulate crowds. Thus, depending on the complexity of the simulation, simple behaviors can be sufficient to simulate crowds. Otherwise, more complicated behavioral rules can be necessary, and in this case, it can be included in the simulation data in order to improve the realism of the animation. We present three different ways for controlling crowd behaviors: i) by using innate and scripted behaviors; ii) by defining behavioral rules, using events and reactions, and, iii) by providing an external control to guide crowd behaviors in real time. The two main contributions of our approach are: the possibility of increasing the complexity of group/agent behaviors according to the problem to be simulated, and the hierarchical structure based on groups to compose a crowd.

Computer Graphics Forum, 2004

Crowd simulation for virtual environments offers many challenges centered on the trade-offs between rich behavior, control and computational cost. In this paper we present a new approach to controlling the behavior of agents in a crowd. Our method is scalable in the sense that increasingly complex crowd behaviors can be created without a corresponding increase in the complexity of the agents. Our approach is also more authorable; users can dynamically specify which crowd behaviors happen in various parts of an environment. Finally, the character motion produced by our system is visually convincing. We achieve our aims with a situation-based control structure. Basic agents have very limited behaviors. As they enter new situations, additional, situation-specific behaviors are composed on the fly to enable agents to respond appropriately. The composition is done using a probabilistic mechanism. We demonstrate our system with three environments including a city street and a theater.

Advances in Computer Graphics, 2021

Simulating virtual crowds is an important challenge in many areas such as games and virtual reality applications. A lot of effort has been dedicated to improving pathfinding, collision avoidance, or decision making, to achieve more realistic human-like behavior. However, crowd simulation will be far from appearing realistic as long as virtual humans are limited to walking animations. Including animation variety could greatly enhance the plausibility of the populated environment. In this paper, we evaluated to what extend animation variety can affect the perceived level of realism of a crowd, regardless of the appearance of the virtual agents (bots vs. humanoids). The goal of this study is to provide recommendations for crowd animation and rendering when simulating crowds. Our results show that the perceived realism of the crowd trajectories and animations is significantly higher when using a variety of animations as opposed to simply having locomotion animations, but only if we render realistic humanoids. If we can only render agents as bots, then there is no much gain from having animation variety, in fact, it could potentially lower the perceived quality of the trajectories.

lesia.univbiskra.net

Abstract: The aim our work is to create virtual humans as intelligent entities, which includes approximate the maximum as possible the virtual agent animation to the natural human behavior. In order to accomplish this task, our agent must be capable to interact with the environment, ...

PsycEXTRA Dataset, 2000

Crowd simulation is an essential component of military training and operations. In this paper, we present an approach for generating 3D visualizations and animations from simulation results in the area of agent-based crowd simulation for military operations. The main contribution is to create a simulation bridge between a federated agent-based crowd simulation architecture and a game engine to drive the visualization and animation of virtual crowd in a 3-dimentional space. In our simulation bridge framework, we present a method for data acquisition to create the virtual environment. An interfacing methodology is introduced between the 3D virtual environment maps and the federated agent-based crowd simulator. Our method comprises of converting the 3D maps to multi-level 2D maps to feed the 2D agent-based crowd simulator's virtual world representation. A tool was implemented for defining objects in the virtual environment based on 2D maps and a color coder scheme. Our bridge framework makes use of a game engine as the visualization medium. The 3D virtual environment maps are reconstructed within the game engine and human avatars are created to simulate the agents. Motion, expression and behavioral animations are attached to each virtual agent. At run-time, data is fed from the 2D crowd simulator to our framework via an event-driven file buffer. Virtual agents are created at run-time based on the social aspects of the crowd composition. Behavioral animation is triggered on each agent based on the commands represented in the simulation results. Our framework also provides visualization of emergency situations and makes use of particle dynamics to generate more realistic visuals of disaster situations and human crowd behavior in an emergency situation.

Lecture Notes in Computer Science, 2016

Realistic animation of agents' activities in a 3D virtual environment has many useful applications, for examples, creative industries, urban planning, military simulation and disaster management. It is tedious to manually pre-program each agent's actions, its interactions with other agents and with the environment. Simulation is a good approach in this kind of domain since complex global behaviors emerge from the local interactions. We simulate a crowd movement using a multi-agent approach where each agent is situated in the virtual environment. An agent can perceive and interact with other agents and with the environment. Complex behaviors emerging from these interactions are from local rules and without any central control. These behaviors reveal the complexity of the domain without explicitly programming the system. In this work, we investigate (i) the navigation of the agents and (ii) the corresponding animations of each agent's behaviors. Simulation results under different parameters are presented and discussed.

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. 1

Lecture Notes in Computer Science, 2003

This paper presents a model to generate and animate groups which emerge as a function of interaction among virtual agents. The agents are characterized through the following parameters: sociability, communication, comfort, perception and memory. The emergent groups are characterized through the cohesion parameter which describes the homogeneity of ideas of the group members. In this work we are mainly interested in investigating the formation of groups (membership and time for grouping), the groups characterization (cohesion parameter) and their visual representation (group formation). The overall results suggest that the interaction among agents contributes to larger groups and higher crowd cohesion values.