Simulating gaze attention behaviors for crowds

Crowd animation is a topic of high interest which offers many challenges. One of the most important is the trade-off between rich, realistic behaviors, and computational costs. To this end, much effort has been put into creating variety in character representation and animation. Nevertheless, one aspect still lacking realism in virtual crowd characters resides in their attention behaviors. In this paper, we propose a framework to add gaze attention behaviors to crowd animations. First, We automatically extract interest points from character or object trajectories in pre-existing animations. For a given character, We assign a set of elementary scores based on parameters such as distance or speed to all other characters or objects in the scene. We then combine these subscores in all overall scoring function. The scores obtained from this function form a set of gaze constraints that determine where and when each character should look. We finally enforce these constraints With all optimized dedicated gaze Inverse Kinematics (IK) solver. It first computes Me displacement maps for the constraints to be satisfied. It then smoothly propagates these displacements over all automatically defined number of frames. We demonstrate the efficiency of our method and our visually convincing results through various examples. Copyright (C) 2009 John Wiley & Sons, Ltd

The impact of animations in the perception of a simulated crowd

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.Peer ReviewedPostprint (author's final draft

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

How simple rules determine pedestrian behavior and crowd disasters

With the increasing size and frequency of mass events, the study of crowd disasters and the simulation of pedestrian flows have become important research areas. Yet, even successful modeling approaches such as those inspired by Newtonian force models are still not fully consistent with empirical observations and are sometimes hard to calibrate. Here, a novel cognitive science approach is proposed, which is based on behavioral heuristics. We suggest that, guided by visual information, namely the distance of obstructions in candidate lines of sight, pedestrians apply two simple cognitive procedures to adapt their walking speeds and directions. While simpler than previous approaches, this model predicts individual trajectories and collective patterns of motion in good quantitative agreement with a large variety of empirical and experimental data. This includes the emergence of self-organization phenomena, such as the spontaneous formation of unidirectional lanes or stop-and-go waves. Moreover, the combination of pedestrian heuristics with body collisions generates crowd turbulence at extreme densities-a phenomenon that has been observed during recent crowd disasters. By proposing an integrated treatment of simultaneous interactions between multiple individuals, our approach overcomes limitations of current physics-inspired pair interaction models. Understanding crowd dynamics through cognitive heuristics is therefore not only crucial for a better preparation of safe mass events. It also clears the way for a more realistic modeling of collective social behaviors, in particular of human crowds and biological swarms. Furthermore, our behavioral heuristics may serve to improve the navigation of autonomous robots.Comment: Article accepted for publication in PNA

Gaze Behaviors for Virtual Crowd Characters

Nowadays, crowds of virtual characters are used in many domains such as neurosciences, psychology, and computer sciences. Since as human beings, we are natural experts in human being representation and movement, it makes it that much harder to correctly model and animate virtual characters. This becomes even more challenging when considering crowds of virtual characters. Indeed, in addition to the representation and animation, there is the mandatory trade-off between rich, realistic behaviors and computational costs. In this paper, we present a crowd engine, to which we introduce and extra layer which allows its characters to produce gaze behaviors. We thus enhance crowd realism by allowing the characters composing it to be aware of their environment and other characters and/or a user

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Visual cognition during real social interaction

Copyright @ 2012 The Authors. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and 85 reproduction in any medium, provided the original author and source are credited. The article was made available through the Brunel University Open Access Publishing Fund.This article has been made available through the Brunel Open Access Publishing Fund.Laboratory studies of social visual cognition often simulate the critical aspects of joint attention by having participants interact with a computer-generated avatar. Recently, there has been a movement toward examining these processes during authentic social interaction. In this review, we will focus on attention to faces, attentional misdirection, and a phenomenon we have termed social inhibition of return (Social IOR), that have revealed aspects of social cognition that were hitherto unknown. We attribute these discoveries to the use of paradigms that allow for more realistic social interactions to take place. We also point to an area that has begun to attract a considerable amount of interest—that of Theory of Mind (ToM) and automatic perspective taking—and suggest that this too might benefit from adopting a similar approach

Simulating interactions with virtual characters for the treatment of social phobia

Virtual Reality (VR) has nowadays become a very useful tool for therapists in the treatment of phobias. Indeed, it allows the simulation of scenarios which are difficult to reproduce in real life. It also allows for a situation to be repeated as much as one wants. Moreover, it allows for a complete control over the situation. The simulation can be stopped if the patient cannot handle it. It can also be tweaked for gradual exposure. Virtual Reality Exposure Therapy (VRET) has proven to be efficient in the context of phobias such as acrophobia or the fear of flying. Social phobia, however, are much harder to deal with. Indeed, as humans, we are experts in human representations and behaviors; it makes it much harder to obtain credible and immersive environments. In this thesis, we describe a set of tools and applications which we have developed to be used in VRET of social phobia and agoraphobia with crowds. We first describe how we create different scenarios for VRET of social phobia. We then expose the application we have developed which allows for elaborate interactions between a user and virtual characters. In particular, we have designed and implemented a software which allows for virtual characters to change behavior depending on the user's eye contact behavior. It allows them to seem interested when being looked at and distracted when not. We then describe the model we have implemented to simulate gaze attention behaviors for crowds of virtual characters. This consists of a method that automatically detects where and when each virtual character in a crowd should look. Secondly, it consists of a dedicated gaze Inverse Kinematics (IK) solver in order for the virtual characters to satisfy the constraints defined by the automatically detected points to be looked at. This allows for the characters to perform the looking motion in a natural and human like way. We then describe the architecture we have developed to combine the work we have done in the domain of social phobia and this model of attention behaviors for crowd characters. We thus use our model of looking behaviors to allow for crowd characters to look at each other. We also use eye-tracking and optical motion capture to determine where a user is looking in a CAVE environment. The virtual characters then respond by either looking at the user, looking at what the user is looking at, or looking at other characters in the crowd. We thus obtain an immersive and interactive environment for VRET in the domain of agoraphobia with crowds. The third part of this thesis describes various experiments we have conducted in order to validate our applications. Our first study consists of using VR in a head-mounted display (HMD) for the treatment of social phobia. In this study, we also use eye-tracking in order to analyze eye contact avoidance behaviors before and after therapy. We then discuss the use of eye-tracking as a tool to help assess and diagnose social phobia. Since eye contact avoidance behaviors are frequent in people suffering from such phobias, eye-tracking can certainly be a helpful tool. We describe an experiment in which we tested eye-tracking as a diagnosis and assessment tool on a phobic population and on a control group. We also describe an experiment to evaluate the potential of our proposed interaction loop in the context of social phobia. Finally, we describe the experiment we have conducted to evaluate our application in the context of agoraphobia with crowds