Reactive pedestrian path following from examples

To present an accurate and compelling view of a new environment, architectural and urban planning applications both require animations of people. Ideally, these animations would be easy for a non-programmer to construct, just as buildings and streets can be modeled by an architect or artist using commercial modeling software. In this paper we explore an approach for generating reactive path following based on the user's examples of the desired behavior. The examples are used to build a model of the desired reactive behavior. The model is combined with reactive control methods to produce natural 2D pedestrian trajectories. The system then automatically generates 3D pedestrian locomotion using motion capture resequencing algorithms. We discuss the accuracy of the model of pedestrian motion and show that simple direction primitives can be recorded and used to build natural, reactive, path-following behaviors.

[1]  Ronald C. Arkin,et al.  Motor schema based navigation for a mobile robot: An approach to programming by behavior , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[2]  James F. Cremer,et al.  VRLOCO: Real-Time Human Locomotion from Positional Input Streams , 1996, Presence: Teleoperators & Virtual Environments.

[3]  Irfan A. Essa,et al.  Controlled animation of video sprites , 2002, SCA '02.

[4]  M. Batty,et al.  Local movement: agent-based models of pedestrian flows , 1998 .

[5]  Bruce H. Krogh,et al.  Integrated path planning and dynamic steering control for autonomous vehicles , 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[6]  Thomas G. Dietterich What is machine learning? , 2020, Archives of Disease in Childhood.

[7]  Irfan A. Essa,et al.  Machine Learning for Video-Based Rendering , 2000, NIPS.

[8]  Soraia Raupp Musse,et al.  Guiding and Interacting with Virtual Crowds in Real-time , 1999 .

[9]  D. M. Lyons,et al.  Tagged potential fields: An approach to specification of complex manipulator configurations , 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

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

[11]  Richard Szeliski,et al.  Video textures , 2000, SIGGRAPH.

[12]  Jean-Claude Latombe,et al.  Robot motion planning , 1970, The Kluwer international series in engineering and computer science.

[13]  Demetri Terzopoulos,et al.  Artificial fishes: physics, locomotion, perception, behavior , 1994, SIGGRAPH.

[14]  Okan Arikan,et al.  Interactive motion generation from examples , 2002, ACM Trans. Graph..

[15]  Franco Tecchia,et al.  Agent Behaviour Simulator (ABS):a platform for urban behaviour development , 2001 .

[16]  Siome Goldenstein,et al.  Non-linear dynamical system approach to behavior modeling , 1999, The Visual Computer.

[17]  John S. McCaskill,et al.  Cellular Automata Model Of Emergent Collective Bi-Directional Pedestrian Dynamics , 2000 .

[18]  Gunnar G. Løvås,et al.  Modeling and Simulation of Pedestrian Traffic Flow , 1994 .

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

[20]  Bruce Blumberg,et al.  Multi-level direction of autonomous creatures for real-time virtual environments , 1995, SIGGRAPH.

[21]  Sung Yong Shin,et al.  Planning biped locomotion using motion capture data and probabilistic roadmaps , 2003, TOGS.

[22]  Soraia Raupp Musse,et al.  A Model of Human Crowd Behavior : Group Inter-Relationship and Collision Detection Analysis , 1997, Computer Animation and Simulation.

[23]  O. Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[24]  James J. Kuffner,et al.  Goal-Directed Navigation for Animated Characters Using Real-Time Path Planning and Control , 1998, CAPTECH.

[25]  Daniel Thalmann,et al.  A paradigm for controlling virtual humans in urban environment simulations , 2000, Appl. Artif. Intell..

[26]  P G Gipps,et al.  A micro simulation model for pedestrian flows , 1985 .

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

[28]  Bruce Blumberg,et al.  Integrated learning for interactive synthetic characters , 2002, SIGGRAPH.

[29]  Jessica K. Hodgins,et al.  Animating Athletic Motion Planning By Example , 2000, Graphics Interface.

[30]  Jessica K. Hodgins,et al.  Interactive control of avatars animated with human motion data , 2002, SIGGRAPH.

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

[32]  Hjp Harry Timmermans,et al.  Towards a multi-agent system for visualising simulated behaviour within the built environment , 2000 .

[33]  Dirk Helbing A Fluid-Dynamic Model for the Movement of Pedestrians , 1992, Complex Syst..

[34]  John Funge,et al.  Cognitive modeling: knowledge, reasoning and planning for intelligent characters , 1999, SIGGRAPH.

[35]  Norman I. Badler,et al.  Pedestrians: creating agent behaviors through statistical analysis of observation data , 2001, Proceedings Computer Animation 2001. Fourteenth Conference on Computer Animation (Cat. No.01TH8596).

[36]  Dimitris N. Metaxas,et al.  Automating gait generation , 2001, SIGGRAPH.

[37]  L. F. Henderson On the fluid mechanics of human crowd motion , 1974 .

[38]  Craig W. Reynolds Flocks, herds, and schools: a distributed behavioral model , 1998 .

[39]  Ken Perlin,et al.  Improv: a system for scripting interactive actors in virtual worlds , 1996, SIGGRAPH.

[40]  Bonnie Webber,et al.  Animation through Reactions, Transition Nets and Plans , 1995 .