Funda Durupinar

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.

A Virtual Garment Design and Simulation System

In this paper, a 3D graphics environment for virtual garment design and simulation is presented. The proposed system enables the three dimensional construction of a garment from its cloth panels, for which the underlying structure is a mass-spring model. The garment construction process is performed through automatic pattern generation, posterior correction, and seaming. Afterwards, it is possible to do fitting on virtual mannequins as if in a real life tailors workshop. The system provides the users with the flexibility to design their own garment patterns and make changes on the garment even after the dressing of the model. Furthermore, rendering alternatives for the visualization of knitted and woven fabric are presented.

Psychological Parameters for Crowd Simulation: From Audiences to Mobs

In the social psychology literature, crowds are classified as audiences and mobs. Audiences are passive crowds, whereas mobs are active crowds with emotional, irrational and seemingly homogeneous behavior. In this study, we aim to create a system that enables the specification of different crowd types ranging from audiences to mobs. In order to achieve this goal we parametrize the common properties of mobs to create collective misbehavior. Because mobs are characterized by emotionality, we describe a framework that associates psychological components with individual agents comprising a crowd and yields emergent behaviors in the crowd as a whole. To explore the effectiveness of our framework we demonstrate two scenarios simulating the behavior of distinct mob types.

Short Communication: Procedural visualization of knitwear and woven cloth

In this paper, a procedural method for the visualization of knitted and woven fabrics is presented. The proposed method is compatible with a mass-spring model and makes use of the regular warp-weft structure of the cloth. The visualization parameters for the loops and threads are easily mapped to the animated mass-spring model. The simulation idea underlying both knitted and woven fabrics is similar as we represent both structures in 3D. As the proposed method is simple and practical, we can achieve near real-time rendering performance with good visual quality.

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 feature-rich 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.

PERFORM: Perceptual Approach for Adding OCEAN Personality to Human Motion Using Laban Movement Analysis

A major goal of research on virtual humans is the animation of expressive characters that display distinct psychological attributes. Body motion is an effective way of portraying different personalities and differentiating characters. The purpose and contribution of this work is to describe a formal, broadly applicable, procedural, and empirically grounded association between personality and body motion and apply this association to modify a given virtual human body animation that can be represented by these formal concepts. Because the body movement of virtual characters may involve different choices of parameter sets depending on the context, situation, or application, formulating a link from personality to body motion requires an intermediate step to assist generalization. For this intermediate step, we refer to Laban Movement Analysis, which is a movement analysis technique for systematically describing and evaluating human motion. We have developed an expressive human motion generation system with the help of movement experts and conducted a user study to explore how the psychologically validated OCEAN personality factors were perceived in motions with various Laban parameters. We have then applied our findings to procedurally animate expressive characters with personality, and validated the generalizability of our approach across different models and animations via another perception study.

What’s Next? The New Era of Autonomous Virtual Humans

This paper identifies several key limitations in the representation, control, locomotion, and authoring of autonomous virtual humans that must be addressed to enter the new age of interactive virtual world applications. These limitations include simplified particle representations of agents which decouples control and locomotion, the lack of multi-modal perception in virtual environments, the need for multiple levels of control granularity, homogeneity in character animation, and monolithic agent architectures which cannot scale to complex multi-agent interactions and global narrative constraints. We present this broad perspective with the objective of providing the stimulus for an exciting new era of virtual human research.

Visualization of crowd synchronization on footbridges

This paper proposes a framework for the visualization of crowd walking synchronization on footbridges. The bridge is modeled as a mass-spring system, which is a weakly damped and driven harmonic oscillator. Both the bridge and the pedestrians walking on the bridge are affected by the movement of each other. The crowd acts according to local behavioral rules. Each pedestrian is provided with a kinematic walking system. We extend a current mathematical model of crowd synchronization on footbridges to include human walking model and crowd simulation techniques. We run experiments to evaluate the influence of these extensions on synchronization.Graphical Abstract

Causal interactions from proteomic profiles: molecular data meets pathway knowledge

Measurement of changes in protein levels and in post-translational modifications, such as phosphorylation, can be highly informative about the phenotypic consequences of genetic differences or about the dynamics of cellular processes. Typically, such proteomic profiles are interpreted intuitively or by simple correlation analysis. Here, we present a computational method to generate causal explanations for proteomic profiles using prior mechanistic knowledge in the literature, as recorded in cellular pathway maps. To demonstrate its potential, we use this method to analyze the cascading events after EGF stimulation of a cell line, to discover new pathways in platelet activation, to identify influential regulators of oncoproteins in breast cancer, to describe signaling characteristics in predefined subtypes of ovarian and breast cancers, and to highlight which pathway relations are most frequently activated across 32 cancer types. Causal pathway analysis, that combines molecular profiles with prior biological knowledge captured in computational form, may become a powerful discovery tool as the amount and quality of cellular profiling rapidly expands. The method is freely available at http://causalpath.org.