Multiple Virtual Human Interactions

Autonomous virtual humans need to be able to interact between each others in virtual environments. These interactions are essentials for the generation of realistic behaviours from virtual humans. This chapter presents a review about interactions between real and multiple virtual humans, as well as between themselves. After presenting the problematics and approaches raised by virtual humans interactions, different methods for simulating such interactions are discussed. Interactions between real and multiple virtual humans are presented first with a focus on virtual assistants and social phobia examples. Interactions between virtual humans are then adressed, particularly gaze attention of other characters and navigation interactions between multiple virtual humans.

[1]  Christopher E. Peters,et al.  Modeling Groups of Plausible Virtual Pedestrians , 2009, IEEE Computer Graphics and Applications.

[2]  A. Schadschneider,et al.  Discretization effects and the influence of walking speed in cellular automata models for pedestrian dynamics , 2004 .

[3]  Marc Cavazza,et al.  Character-Based Interactive Storytelling , 2002, IEEE Intell. Syst..

[4]  Leonidas J. Guibas,et al.  Shape google: Geometric words and expressions for invariant shape retrieval , 2011, TOGS.

[5]  Stéphane Donikian,et al.  A synthetic-vision based steering approach for crowd simulation , 2010, SIGGRAPH 2010.

[6]  Dinesh Manocha,et al.  Simulating heterogeneous crowd behaviors using personality trait theory , 2011, SCA '11.

[7]  Demetri Terzopoulos,et al.  A decision network framework for the behavioral animation of virtual humans , 2007, SCA '07.

[8]  Daniel Thalmann,et al.  Navigation for digital actors based on synthetic vision, memory, and learning , 1995, Comput. Graph..

[9]  J. Drury,et al.  Modelling subgroup behaviour in crowd dynamics DEM simulation , 2009 .

[10]  Cécile Appert-Rolland,et al.  Traffic Instabilities in Self-Organized Pedestrian Crowds , 2012, PLoS Comput. Biol..

[11]  Kevin O'Brien,et al.  Human Behavior Models for Agents in Simulators and Games: Part I: Enabling Science with PMFserv , 2006, Presence: Teleoperators & Virtual Environments.

[12]  Stéphane Donikian,et al.  Experiment-based modeling, simulation and validation of interactions between virtual walkers , 2009, SCA '09.

[13]  M. Krijn,et al.  Virtual reality exposure therapy of anxiety disorders: a review. , 2004, Clinical psychology review.

[14]  A. Schadschneider,et al.  Simulation of pedestrian dynamics using a two dimensional cellular automaton , 2001 .

[15]  Dirk Helbing,et al.  Specification of the Social Force Pedestrian Model by Evolutionary Adjustment to Video Tracking Data , 2007, Adv. Complex Syst..

[16]  J. Pettré,et al.  Properties of pedestrians walking in line: fundamental diagrams. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  Michal Ponder,et al.  Building Exposure: Synergy of Interaction and Narration Through the Social Channel , 2005, Presence: Teleoperators & Virtual Environments.

[18]  Allen Newell,et al.  SOAR: An Architecture for General Intelligence , 1987, Artif. Intell..

[19]  D. Thalmann,et al.  Using physiological measures for emotional assessment: A computer-aided tool for cognitive and behavioural therapy , 2004 .

[20]  Norman I. Badler,et al.  Smart Events and Primed Agents , 2010, IVA.

[21]  A. Schadschneider.  Cellular Automaton Approach to Pedestrian Dynamics - Theory , 2001, cond-mat/0112117.

[22]  Céline Loscos,et al.  Intuitive crowd behavior in dense urban environments using local laws , 2003, Proceedings of Theory and Practice of Computer Graphics, 2003..

[23]  Norman I. Badler,et al.  Parameterizing Behavior Trees , 2011, MIG.

[24]  Mark H. Overmars,et al.  Simulating and Evaluating the Local Behavior of Small Pedestrian Groups , 2012, IEEE Transactions on Visualization and Computer Graphics.

[25]  M Tsiknakis,et al.  JUST in time health emergency interventions: an innovative approach to training the citizen for emergency situations using virtual reality techniques and advanced IT tools (the Web-CD). , 2004, Studies in health technology and informatics.

[26]  Craig W. Reynolds.  Flocks, herds, and schools: a distributed behavioral model , 1987, SIGGRAPH.

[27]  Bernd Schmidt,et al.  PECS - Agent-Based Modelling of Human Behaviour , 2001 .

[28]  W. Daamen.  QUALITATIVE RESULTS FROM PEDESTRIAN LABORATORY EXPERIMENTS , 2003 .

[29]  Mark H. Overmars,et al.  A Velocity-Based Approach for Simulating Human Collision Avoidance , 2010, IVA.

[30]  Ron Sun,et al.  Cognition and Multi-Agent Interaction: The CLARION Cognitive Architecture: Extending Cognitive Modeling to Social Simulation , 2005 .

[31]  A Turner.  To move through space: lines of vision and movement , 2007 .

[32]  Ming C. Lin,et al.  Hybrid Long-Range Collision Avoidance for Crowd Simulation , 2013, IEEE Transactions on Visualization and Computer Graphics.

[33]  Daniel Thalmann,et al.  Hierarchical Model for Real Time Simulation of Virtual Human Crowds , 2001, IEEE Trans. Vis. Comput. Graph..

[34]  Soraia Raupp Musse,et al.  Modeling individual behaviors in crowd simulation , 2003, Proceedings 11th IEEE International Workshop on Program Comprehension.

[35]  Daniel Thalmann,et al.  Virtual reality as a therapeutic tool in the confines of social anxiety disorder treatment , 2006 .

[36]  Jan M. Allbeck,et al.  Populations with Purpose , 2011, MIG.

[37]  Axel Klar,et al.  Derivation of Continuum Traffic Flow Models from Microscopic Follow-the-Leader Models , 2002, SIAM J. Appl. Math..

[38]  Dinesh Manocha,et al.  Reciprocal n-Body Collision Avoidance , 2011, ISRR.

[39]  David N. Lee,et al.  A Theory of Visual Control of Braking Based on Information about Time-to-Collision , 1976, Perception.

[40]  Sébastien Paris,et al.  Activity-Driven Populace: A Cognitive Approach to Crowd Simulation , 2009, IEEE Computer Graphics and Applications.

[41]  Katsuya Yamori.  Going with the flow : Micro-macro dynamics in the macrobehavioral patterns of pedestrian crowds , 1998 .

[42]  Leslie Pack Kaelbling,et al.  A Dynamical Model of Visually-Guided Steering, Obstacle Avoidance, and Route Selection , 2003, International Journal of Computer Vision.

[43]  J. S. Wiggins,et al.  The five-factor model of personality : theoretical perspectives , 1996 .

[44]  Joshi Neel,et al.  画像の例を用いた個人写真の強調 | 文献情報 | J-GLOBAL 科学技術総合リンクセンター , 2010 .

[45]  Stéphane Bouchard,et al.  Virtual Reality Therapy Versus Cognitive Behavior Therapy for Social Phobia: A Preliminary Controlled Study , 2005, Cyberpsychology Behav. Soc. Netw..

[46]  Helena Grillon,et al.  Simulating gaze attention behaviors for crowds , 2009 .

[47]  Daniel Thalmann,et al.  An immersive multi-agent system for interactive applications , 2012, The Visual Computer.

[48]  Daniel Thalmann,et al.  A vision-based approach to behavioural animation , 1990, Comput. Animat. Virtual Worlds.

[49]  Daniel Thalmann,et al.  Direct 3D interaction with smart objects , 1999, VRST '99.

[50]  D. Helbing,et al.  The Walking Behaviour of Pedestrian Social Groups and Its Impact on Crowd Dynamics , 2010, PloS one.

[51]  John R Anderson,et al.  An integrated theory of the mind. , 2004, Psychological review.

[52]  Petros Faloutsos,et al.  Situation agents: agent-based externalized steering logic , 2010 .

[53]  Xiaolin Hu,et al.  Modeling group structures in pedestrian crowd simulation , 2010, Simul. Model. Pract. Theory.

[54]  Adrien Bousseau,et al.  Real-time rough refraction , 2011, SI3D.

[55]  Cécile Appert-Rolland,et al.  Realistic following behaviors for crowd simulation , 2012, Comput. Graph. Forum.

[56]  M Fato,et al.  JUST in time health emergency interventions: an innovative approach to training the citizen for emergency situations using virtual reality techniques and advanced IT tools (the VR Tool). , 2004, Studies in health technology and informatics.

[57]  M. Slater,et al.  An experiment on fear of public speaking in virtual reality. , 2001, Studies in Health Technology and Informatics.

[58]  Daniel Thalmann,et al.  Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/cav.148 , 2022 .

[59]  Daniel Thalmann,et al.  Sensor-based synthetic actors in a tennis game simulation , 1998, The Visual Computer.

[60]  A. Newell.  Unified Theories of Cognition , 1990 .

[61]  Julien Pettré,et al.  Imperceptible relaxation of collision avoidance constraints in virtual crowds , 2011, ACM Trans. Graph..

[62]  A. Seyfried,et al.  The fundamental diagram of pedestrian movement revisited , 2005, physics/0506170.

[63]  Brett R. Fajen,et al.  Affordance-Based Control of Visually Guided Action , 2007 .

[64]  Stéphane Donikian,et al.  Crowd of Virtual Humans: a New Approach for Real Time Navigation in Complex and Structured Environments , 2004, Comput. Graph. Forum.

[65]  Jur P. van den Berg,et al.  Meso-scale planning for multi-agent navigation , 2013, 2013 IEEE International Conference on Robotics and Automation.

[66]  Helbing,et al.  Social force model for pedestrian dynamics. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[67]  Sébastien Paris,et al.  Pedestrian Reactive Navigation for Crowd Simulation: a Predictive Approach , 2007, Comput. Graph. Forum.

[68]  Petros Faloutsos,et al.  Egocentric affordance fields in pedestrian steering , 2009, I3D '09.

[69]  Tsai-Yen Li,et al.  Simulating virtual human crowds with a leader-follower model , 2001, Proceedings Computer Animation 2001. Fourteenth Conference on Computer Animation (Cat. No.01TH8596).

[70]  Stéphane Donikian,et al.  Automatic orchestration of behaviours through the management of resources and priority levels , 2002, AAMAS '02.

[71]  Stéphane Donikian,et al.  A Local Behavior Model for Small Pedestrian Groups , 2011, 2011 12th International Conference on Computer-Aided Design and Computer Graphics.