Generating Plausible Individual Agent Movements From Spatio-Temporal Occupancy Data

We introduce the Spatio-Temporal Agent Motion Model, a datadriven representation of the behavior and motion of individuals within a space over the course of a day. We explore different representations for this model, incorporating different modes of individual behavior, and describe how crowd simulations can use this model as source material for dynamic and realistic behaviors

Populating 3D Cities: a True Challenge

In this paper, we describe how we can model crowds in real-time using dynamic meshes, static meshes andimpostors. Techniques to introduce variety in crowds including colors, shapes, textures, individualanimation, individualized path-planning, simple and complex accessories are explained. We also present ahybrid architecture to handle the path planning of thousands of pedestrians in real time, while ensuringdynamic collision avoidance. Several behavioral aspects are presented as gaze control, group behaviour, aswell as the specific technique of crowd patches

Populating 3D Cities: A True Challenge

In this paper, we describe how we can model crowds in real-time using dynamic meshes, static meshes andimpostors. Techniques to introduce variety in crowds including colors, shapes, textures, individualanimation, individualized path-planning, simple and complex accessories are explained. We also present ahybrid architecture to handle the path planning of thousands of pedestrians in real time, while ensuringdynamic collision avoidance. Several behavioral aspects are presented as gaze control, group behaviour, aswell as the specific technique of crowd patches

Multilinear motion synthesis with level-of-detail controls

Interactive animation systems often use a level-of-detail(LOD) control to reduce the computational cost by eliminatingunperceivable details of the scene. Most methodsemploy a multiresolutional representation of animationand geometrical data, and adaptively change the accuracylevel according to the importance of each character.Multilinear analysis provides the efficient representation ofmultidimensional and multimodal data, including humanmotion data, based on statistical data correlations. Thispaper proposes a LOD control method of motion synthesiswith a multilinear model. Our method first extracts asmall number of principal components of motion samplesby analyzing three-mode correlations among joints, time,and samples using high-order singular value decomposition.A new motion is synthesized by interpolatingthe reduced components using geostatistics, where theprediction accuracy of the resulting motion is controlledby adaptively decreasing the data dimensionality. Weintroduce a hybrid algorithm to optimize the reductionsize and computational time according to the distancefrom the camera while maintaining visual quality. Ourmethod provides a practical tool for creating an interactiveanimation of many characters while ensuring accurate andflexible controls at a modest level of computational cost

Controlling Individual Agents in High-Density Crowd Simulation

Simulating the motion of realistic, large, dense crowds of autonomous agents is still a challenge for the computer graphics community. Typical approaches either resemble particle simulations (where agents lack orientation controls) or are conservative in the range of human motion possible (agents lack psychological state and aren’t allowed to ‘push’ each other). Our HiDAC system (for High-Density Autonomous Crowds) focuses on the problem of simulating the local motion and global wayfinding behaviors of crowds moving in a natural manner within dynamically changing virtual environments. By applying a combination of psychological and geometrical rules with a social and physical forces model, HiDAC exhibits a wide variety of emergent behaviors from agent line formation to pushing behavior and its consequences; relative to the current situation, personalities of the individuals and perceived social density

Motion In-Betweening with Phase Manifolds

This paper introduces a novel data-driven motion in-betweening system to reach target poses of characters by making use of phases variables learned by a Periodic Autoencoder. Our approach utilizes a mixture-of-experts neural network model, in which the phases cluster movements in both space and time with different expert weights. Each generated set of weights then produces a sequence of poses in an autoregressive manner between the current and target state of the character. In addition, to satisfy poses which are manually modified by the animators or where certain end effectors serve as constraints to be reached by the animation, a learned bi-directional control scheme is implemented to satisfy such constraints. The results demonstrate that using phases for motion in-betweening tasks sharpen the interpolated movements, and furthermore stabilizes the learning process. Moreover, using phases for motion in-betweening tasks can also synthesize more challenging movements beyond locomotion behaviors. Additionally, style control is enabled between given target keyframes. Our proposed framework can compete with popular state-of-the-art methods for motion in-betweening in terms of motion quality and generalization, especially in the existence of long transition durations. Our framework contributes to faster prototyping workflows for creating animated character sequences, which is of enormous interest for the game and film industry.Comment: 17 pages, 11 figures, conferenc

Example Based Caricature Synthesis

The likeness of a caricature to the original face image is an essential and often overlooked part of caricature production. In this paper we present an example based caricature synthesis technique, consisting of shape exaggeration, relationship exaggeration, and optimization for likeness. Rather than relying on a large training set of caricature face pairs, our shape exaggeration step is based on only one or a small number of examples of facial features. The relationship exaggeration step introduces two definitions which facilitate global facial feature synthesis. The first is the T-Shape rule, which describes the relative relationship between the facial elements in an intuitive manner. The second is the so called proportions, which characterizes the facial features in a proportion form. Finally we introduce a similarity metric as the likeness metric based on the Modified Hausdorff Distance (MHD) which allows us to optimize the configuration of facial elements, maximizing likeness while satisfying a number of constraints. The effectiveness of our algorithm is demonstrated with experimental results

Automated generation of geometrically-precise and semantically-informed virtual geographic environnements populated with spatially-reasoning agents

La Géo-Simulation Multi-Agent (GSMA) est un paradigme de modélisation et de simulation de phénomènes dynamiques dans une variété de domaines d'applications tels que le domaine du transport, le domaine des télécommunications, le domaine environnemental, etc. La GSMA est utilisée pour étudier et analyser des phénomènes qui mettent en jeu un grand nombre d'acteurs simulés (implémentés par des agents) qui évoluent et interagissent avec une représentation explicite de l'espace qu'on appelle Environnement Géographique Virtuel (EGV). Afin de pouvoir interagir avec son environnement géographique qui peut être dynamique, complexe et étendu (à grande échelle), un agent doit d'abord disposer d'une représentation détaillée de ce dernier. Les EGV classiques se limitent généralement à une représentation géométrique du monde réel laissant de côté les informations topologiques et sémantiques qui le caractérisent. Ceci a pour conséquence d'une part de produire des simulations multi-agents non plausibles, et, d'autre part, de réduire les capacités de raisonnement spatial des agents situés. La planification de chemin est un exemple typique de raisonnement spatial dont un agent pourrait avoir besoin dans une GSMA. Les approches classiques de planification de chemin se limitent à calculer un chemin qui lie deux positions situées dans l'espace et qui soit sans obstacle. Ces approches ne prennent pas en compte les caractéristiques de l'environnement (topologiques et sémantiques), ni celles des agents (types et capacités). Les agents situés ne possèdent donc pas de moyens leur permettant d'acquérir les connaissances nécessaires sur l'environnement virtuel pour pouvoir prendre une décision spatiale informée. Pour répondre à ces limites, nous proposons une nouvelle approche pour générer automatiquement des Environnements Géographiques Virtuels Informés (EGVI) en utilisant les données fournies par les Systèmes d'Information Géographique (SIG) enrichies par des informations sémantiques pour produire des GSMA précises et plus réalistes. De plus, nous présentons un algorithme de planification hiérarchique de chemin qui tire avantage de la description enrichie et optimisée de l'EGVI pour fournir aux agents un chemin qui tient compte à la fois des caractéristiques de leur environnement virtuel et de leurs types et capacités. Finalement, nous proposons une approche pour la gestion des connaissances sur l'environnement virtuel qui vise à supporter la prise de décision informée et le raisonnement spatial des agents situés

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