Nuria Pelechano

Modeling Crowd and Trained Leader Behavior during Building Evacuation

This article considers animating evacuation in complex buildings by crowds who might not know the structures connectivity, or who find routes accidentally blocked. It takes into account simulated crowd behavior under two conditions: where agents communicate building route knowledge, and where agents take different roles such as trained personnel, leaders, and followers

Creating crowd variation with the OCEAN personality model

Most current crowd simulators animate homogeneous crowds, but include underlying parameters that can be tuned to create variations within the crowd. These parameters, however, are specific to the crowd models and may be difficult for an animator or naive user to use. We propose mapping these parameters to personality traits. In this paper, we extend the HiDAC (High-Density Autonomous Crowds) system by providing each agent with a personality model in order to examine how the emergent behavior of the crowd is affected. We use the OCEAN personality model as a basis for agent psychology. To each personality trait we associate nominal behaviors; thus, specifying personality for an agent leads to an automation of the low-level parameter tuning process. We describe a plausible mapping from personality traits to existing behavior types and analyze the overall emergent crowd behaviors.

How the Ocean Personality Model Affects the Perception of Crowds

This approach extends the HiDAC (High-Density Autonomous Crowds) system by providing each agent with a personality model based on the Ocean (openness, conscientiousness, extroversion, agreeableness, and neuroticism) personality model. Each personality trait has an associated nominal behavior. Specifying an agents personality leads to an automation of low-level parameter tuning.

Multi-domain real-time planning in dynamic environments

This paper presents a real-time planning framework for multi-character navigation that enables the use of multiple heterogeneous problem domains of differing complexities for navigation in large, complex, dynamic virtual environments. The original navigation problem is decomposed into a set of smaller problems that are distributed across planning tasks working in these different domains. An anytime dynamic planner is used to efficiently compute and repair plans for each of these tasks, while using plans in one domain to focus and accelerate searches in more complex domains. We demonstrate the benefits of our framework by solving many challenging multi-agent scenarios in complex dynamic environments requiring space-time precision and explicit coordination between interacting agents, by accounting for dynamic information at all stages of the decision-making process.

NEOGEN: Near optimal generator of navigation meshes for 3D multi-layered environments

Abstract In this paper we introduce a novel automatic method for generating near optimal navigation meshes from a 3D multi-layered virtual environment. Firstly, a GPU voxelization of the entire scene is calculated in order to identify and extract the different walkable layers. Secondly, a high resolution render is performed with a fragment shader to obtain the 2D floor plan of each layer. Finally, a convex decomposition of each layer is calculated and layers are linked in order to create a Navigation Mesh of the scene. Results show that our method is not only faster than the previous work, but also creates more accurate NavMeshes since it respects the original shape of the static geometry. It also provides a significantly lower number of cells and avoids ill-conditioned cells and T-Joints between portals that could lead to unnatural character navigation.

Virtual Crowds: Steps Toward Behavioral Realism

This volume presents novel computational models for representing digital humans and their interactions with other virtual characters and meaningful environments. In this context, we describe efficient algorithms to animate, control, and author human-like agents having their own set of unique capabilities, personalities, and desires. We begin with the lowest level of footstep determination to steer agents in collision-free paths. Steering choices are controlled by navigation in complex environments, including multi-domain planning with dynamically changing situations. Virtual agents are given perceptual capabilities analogous to those of real people, including sound perception, multi-sense attention, and understanding of environment semantics which affect their behavior choices. The roles and impacts of individual attributes, such as memory and personality are explored. The animation challenges of integrating a number of simultaneous behavior and movement demands on an agent are addressed through an open source software system. Finally, the creation of stories and narratives with groups of agents subject to planning and environmental constraints culminates the presentation.

A Grid Representation for Distributed Virtual Environments

Fast Internet connections and the widespread use of high performance graphic cards are making Distributed Virtual Environments (DVE) very common nowadays. The architecture and behavior of these systems are very similar to new grid computing applications where concepts such as sharing and high scalability are extremely exploited. However, there are several key issues in these systems that should still be improved in order to design a scalable and cost-effective DVE system. One of these key issues is the partitioning problem. This problem consists of efficiently assigning clients (3-D avatars) to the arbiters (servers) in the system. As an alternative to the ad-hoc heuristic proposed in the literature, this paper presents a comparison study of two evolutionary heuristics for solving the partitioning problem in DVE systems. Performance evaluation results show that heuristic methods can greatly improve the performance of the partitioning method, particularly for large DVE systems. In this way, efficiency and scalability of DVE systems can be significantly improved.

Automatic generation of suboptimal navmeshes

Most current games perform navigation in virtual environments through A* for path finding combined with a local movement algorithm. Navigation Meshes are the most popular approach to combine path finding with local movement. This paper presents a new Automatic Navigation Mesh Generator (ANavMG) that subdivides any polygon representing the environment, with or without holes, into a suboptimal number of convex cells where local movement algorithms can be applied without deadlocks. We introduce the concept of convex relaxation to further reduce the number of cells depending on the flexibility of the local movement algorithm. Finally we show results of the ANavMG and its application to a multi player game.

Simulating Heterogeneous Crowds with Interactive Behaviors

This book provides a deep understanding of state-of-art methods for simulation of heterogeneous crowds in computer graphics. It will cover different aspects that are necessary to achieve plausible crowd behaviors. The book will be a review of the most recent literature in this field that can help professionals and graduate students interested in this field to get up to date with the latest contributions, and open problems for their possible future research. The chapter contributors are well known researchers and practitioners in the field and they include their latest contributions in the different topics required to achieve believable heterogeneous crowd simulation.

Efficient rendering of animated characters through optimized per-joint impostors

In this paper, we present a new impostor‐based representation for 3D animated characters supporting real‐time rendering of thousands of agents. We maximize rendering performance by using a collection of pre‐computed impostors sampled from a discrete set of view directions. Our approach differs from previous work on view‐dependent impostors in that we use per‐joint rather than per‐character impostors. Our characters are animated by applying the joint rotations directly to the impostors, instead of choosing a single impostor for the whole character from a set of pre‐defined poses. This offers more flexibility in terms of animation clips, as our representation supports any arbitrary pose, and thus, the agent behavior is not constrained to a small collection of pre‐defined clips. Because our impostors are intended to be valid for any pose, a key issue is to define a proper boundary for each impostor to minimize image artifacts while animating the agents. We pose this problem as a variational optimization problem and provide an efficient algorithm for computing a discrete solution as a pre‐process. To the best of our knowledge, this is the first time a crowd rendering algorithm encompassing image‐based performance, small graphics processing unit footprint, and animation independence is proposed. Copyright