Real-time motion planning, navigation, and behavior for large crowds of virtual humans

Simulating crowds in real time is a challenging problem that touches many different aspects of Computer Graphics: rendering, animation, path planning, behavior, etc. Our work has mainly focused on two particular aspects of real-time crowds: motion planning and behavior. Real-time crowd motion planning requires fast, realistic methods for path planning as well as obstacle avoidance. The difficulty to find a satisfying trade-off between efficiency and believability is particularly challenging, and prior techniques tend to focus on a single approach. We have developed two approaches to completely solve crowd motion planning in real time. The first one is a hybrid architecture able to handle the path planning of thousands of pedestrians in real time, while ensuring dynamic collision avoidance. The scalability of this architecture allows to interactively create and distribute regions of varied interest, where motion planning is ruled by different algorithms. Practically, regions of high interest are governed by a long-term potential field-based approach, while other zones exploit a graph of the environment and short-term avoidance techniques. Our architecture also ensures pedestrian motion continuity when switching between motion planning algorithms. Tests and comparisons show that our architecture is able to realistically plan motion for thousands of characters in real time, and in varied environments. Our second approach is based on the concept of motion patches [Lee et al., 2006], that we extend to densely populate large environments. We build a population from a set of blocks containing a pre-computed local crowd simulation. Each block is called a crowd patch. We address the problem of computing patches, assembling them to create virtual environments (VEs), and controlling their content to answer designers' needs. Our major contribution is to provide a drastic lowering of computation needs for simulating a virtual crowd at runtime. We can thus handle dense populations in large-scale environments with performances never reached so far. Our results illustrate the real-time population of a potentially infinite city with realistic and varied crowds interacting with each other and their environment. Enforcing intelligent autonomous behaviors in crowds is a difficult problem, for most algorithms are too computationally expensive to be exploited on large crowds. Our work has been focused on finding solutions that can simulate intelligent behaviors of characters, while remaining computationally inexpensive. We contribute to crowd behaviors by developing situation-based behaviors, i.e., behaviors triggered depending on the position of a pedestrian. We have also extended our crowd motion planning architecture with an algorithm able to simulate group behaviors, which much enhances the user perception of the watched scene

Position-Based Multi-Agent Dynamics for Real-Time Crowd Simulation (MiG paper)

Exploiting the efficiency and stability of Position-Based Dynamics (PBD), we introduce a novel crowd simulation method that runs at interactive rates for hundreds of thousands of agents. Our method enables the detailed modeling of per-agent behavior in a Lagrangian formulation. We model short-range and long-range collision avoidance to simulate both sparse and dense crowds. On the particles representing agents, we formulate a set of positional constraints that can be readily integrated into a standard PBD solver. We augment the tentative particle motions with planning velocities to determine the preferred velocities of agents, and project the positions onto the constraint manifold to eliminate colliding configurations. The local short-range interaction is represented with collision and frictional contact between agents, as in the discrete simulation of granular materials. We incorporate a cohesion model for modeling collective behaviors and propose a new constraint for dealing with potential future collisions. Our new method is suitable for use in interactive games.Comment: 9 page

Group emotion modelling and the use of middleware for virtual crowds in video-games

In this paper we discuss the use of crowd simulation in video-games to augment their realism. Using previous works on emotion modelling and virtual crowds we define a game world in an urban context. To achieve that, we explore a biologically inspired human emotion model, investigate the formation of groups in crowds, and examine the use of physics middleware for crowds. Furthermore, we assess the realism and computational performance of the proposed approach. Our system runs at interactive frame-rate and can generate large crowds which demonstrate complex behaviour

LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning

We present a novel procedural framework to generate an arbitrary number of labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to design accurate algorithms or training models for crowded scene understanding. Our overall approach is composed of two components: a procedural simulation framework for generating crowd movements and behaviors, and a procedural rendering framework to generate different videos or images. Each video or image is automatically labeled based on the environment, number of pedestrians, density, behavior, flow, lighting conditions, viewpoint, noise, etc. Furthermore, we can increase the realism by combining synthetically-generated behaviors with real-world background videos. We demonstrate the benefits of LCrowdV over prior lableled crowd datasets by improving the accuracy of pedestrian detection and crowd behavior classification algorithms. LCrowdV would be released on the WWW

System Issues in Multi-agent Simulation of Large Crowds

Crowd simulation is a complex and challenging domain. Crowds demonstrate many complex behaviours and are consequently difficult to model for realistic simulation systems. Analyzing crowd dynamics has been an active area of research and efforts have been made to develop models to explain crowd behaviour. In this paper we describe an agent based simulation of crowds, based on a continuous field force model. Our simulation can handle movement of crowds over complex terrains and we have been able to simulate scenarios like clogging of exits during emergency evacuation situations. The focus of this paper, however, is on the scalability issues for such a multi-agent based crowd simulation system. We believe that scalability is an important criterion for rescue simulation systems. To realistically model a disaster scenario for a large city, the system should ideally scale up to accommodate hundreds of thousands of agents. We discuss the attempts made so far to meet this challenge, and try to identify the architectural and system constraints that limit scalability. Thereafter we propose a novel technique which could be used to richly simulate huge crowds