1. Crowds (and research in computer animation and games)
CSE 3541
Matt Boggus

2. Foundation of Digital Games
See site for paper topics

3. CASA – computer animation and social agents
Social Agents and Avatars
Emotion and Personality
Virtual Humans
Autonomous Actors
AI-based Animation
Social and Conversational Agents
Inter-Agent Communication
Social Behavior
Crowd Simulation
Understanding Human Activity
Memory and Long-term Interaction, etc.

4. SCA – symposium on computer animation
2D, 3D, and N-D animation systems
autonomous characters
clothing animation and simulation
expressive motion / communication
facial animation
group and crowd behavior
intuitive interfaces for creating and editing animations
mathematical foundations of animation
methods of control and artistic direction of simulations
nature in motion (natural phenomena, plants, clouds, ...)
new time-based art forms on the computer
novel time-varying phenomena
perceptual metrics for animation
perceptual foundations of animation
physical realism / measuring the real world for animation
physical simulation
fluid animation
planning / learning / optimization for animation
real-time and interactive methods
camera control methods for computer animation
sound and speech for animation

5. I3D – symposium on interactive 3D graphics and games
Interaction devices and techniques
3D game techniques
Interactive modeling
Level-of-detail approaches
Pre-computed lighting
Visibility computation
Real-time surface shading
Fast shadows, caustics and reflections
Imposters and image-based techniques
Animated models
GPU techniques
Navigation methods
Interactive visualization
Virtual and augmented reality
User studies of interactive techniques and applications
Sketch based 3D interaction

6. Siggraph
See

7. Crowd modeling applications
Entertainment: Games Computer animation Art Evaluation: Architecture Robotics Training: Virtual reality simulation

8. Individuals and crowds
Individuals are agents
Reactive vs. planning
Goal vs. need driven
Groups – set of similar agents
Spatially close
Like minded (butter-side up or butter-side down)
Crowds – many individuals, with or without groups
Emergent behavior – similar to flocking, flocking system
Uniformity – sameness of members
Quantity & density – average distance between members

9. Agent based example “Autonomous Pedestrians” paper
Emulating real pedestrians in urban environments
Motions controlled at different levels
Reactive behaviors
Navigational and motivational behaviors
Cognitive behaviors
Information stored in mental states
More videos at

10. Autonomous Pedestrian techniques

11. Autonomous Pedestrian techniques

12. Autonomous Pedestrian techniques

13. Crowds example “Continuum Crowds” paper
More videos at

14. Continuum Crowds technique

15. Potential fields – 2 examples
Image sources:

16. Cellular Automata Regular grid of cells, each in a particular state
Each cell has a neighborhood
Set of other nearby cells, typically adjacent
Set of rules dictate how the cells change state based on other cells in their neighborhood
Ex: Conway’s Game of Life

17. Spatial organization Cellular decomposition: Regular 2D grid
Adjacency accessible
Density limited
Cells define obstructions
Continuous space:
Step in any direction
Need to decipher obstructions
Perception needed

18. More papers on crowd simulation
Simulating Heterogeneous Crowd Behaviors Using Personality Trait Theory
Environment-aware Real-time Crowd Control
A Synthetic-Vision-Based Steering Approach for Crowd Simulation (scroll down to find paper and video)

19. Commercial crowd simulation
MASSIVE
Epic Battle Simulator

20. Additional slides

21. Execution environment
Real-time / Interactive
Simple computations
Avoid n2 or higher algorithms
Limit size
Off-line
Can use complex models for behavior
Can allow interaction between all agents (n2)
Size limited only by hardware memory and storage

22. Panic & Congestion handling
Personal space
Packing people during evacuation
Exit awareness

23. Panic & Congestion Example
Autonomous Pedestrians Controlling Agents in High-Density Crowd Simulation

24. Navigation Fluid flow: density fields, potential functions
Particle systems:
Individual navigation
Flocking systems:
individual perception, navigation
Rule-based
Cognitive modeling
Cellular automata

25. Motion & Navigation Path planning Roadmaps Passing on pathways
Potential fields
Forming & maintaining subgroups