1. Situation Based Approach for Virtual Crowd Simulation Ph.D Preliminary talk Mankyu Sung

2. Crowds In Different Environments Sports event Street Museum

3. Features in Crowds Large number of people Share same environment Anonymity Importance of short term crowd behavior Importance of locational factor in crowd behavior Importance of social-relational factor in crowd behavior

4. Applications of Crowd Simulation Training Education Entertainment Architecture

5. Why Crowd Simulation is Hard? -Conflicting Goals -Simple agent with simple behaviors vs. Complex agent with realistic behaviors -Control over action of crowd vs. Not control over every agent individually -Fast simulation of the small number of characters vs. Slow simulation of the large number of characters

6. Talk Outline 1. Research Goal 2. Related Works 3. Works to Date 4. Demo 5. Future Plan

7. The Goal of Research Set three demands that are able to solve these problems. Scalability Controllability Convincingness

8. Scalability Two Specific Scalabilities –Memory Scalability The amount of memory for a character does not proportionally increase as the complexity of environment increases. –Performance Scalability The overall performance (frame-rate) does not proportionally increase as the complexity of environment increases.

9. Convincingness Visually Convincing Behaviors –Visually realistic motion of characters Semantically Convincing Behaviors –Plausible behaviors for given time e.g.) At a crosswalk, crowds are crossing or standing depending on a traffic sign.

10. Controllability Specify crowd behaviors –User interfaces Control crowd flow –Predefined scenario –Interactive control –Density control

11. Proposed Approach Scalability –Situation based simulation Convincingness –STM(Snap-Together-Motion) –Composable behaviors Controllability –Painting interface –Situation graphs (Sung et al. EG2004) Situation Based Approach

12. Thesis Statement It is my thesis that the situation based approach is able to achieve the demand of scalability, convincingness and controllability.

13. Related Works Smart Environments –Smart Object (Kallman et al. 1998) –Informed environment (Farenc et al. 1999) –Informed hierarchical information (Thomas et al. 2000) –Apply Gibson’s “natural movement” theory (Michael et al. 2003) Computer Games –The Sims TM (EA games)

14. Related Works Character Animation –Non-human creature Flocking algorithm : Boids (Reynolds, 1987) Artificial fish by using synthetic vision (Tu et al. 1994) –Human animation Motion blending (Rose et al. 1996, Wiley et al. 1997, Kovar et al. 2003) STM(Snap-Together-Motion) (Gleicher et al. 2003)

15. Related works A1A2A3 Actions Time Behaviors in STM –Behavior is a series of actions over time –Specifying a behavior is to choose proper action one by one in time

16. Related Works Intelligent Agent –Cognitive architecture (Funge 1999) –Role-passing system (Horswill 1999, O’Sullivan et al. 2002, McNames et al. 2003) Crowd Modeling –Rule based system (Musse et al. 1987, 2001) –Cellular automata (Blue et al. 1998)

17. Related Works Crowd Modeling –Physically Based Approach Fluid dynamics (Henderson, 1974) Particle system (Bouvier et al. 1997, Gipps et al. 1985) Social force model (Helbing et al. 1995, 2000) –Robotics Algorithm Use PRM for group behavior (Bayazit et al. 2002) Collision-free path planning for multiple robots (Furtney 2000) Leader-Following model (Li et al. 2001)

18. Situation Based Approach Scalability –Situation based simulation Convincingness –STM(Snap-Together-Motion) –Composable behaviors Controllability –Painting interface –Situation graphs

19. Situations Situation A1A2 A3A8 Agent Behavior 1 Behavior 2 Character Actions A1A2 A3 A4 A8 A7 A6A5 Behavior 1 Behavior 2 Behavior 3...... …

20. Situations (2) Example A man Actions singwalk turn sit climb dance standcross Zig-Zag walk Straight walk Sit down...... … At a crosswalk A man cross street stand Straight walk Checking cars

21. Situation Situations (3) Agent A1 A2 Behavior 1 Behavior 2 A3 A4 Behavior 3 Behavior 4 Behavior 5 Augmented Behaviors Augmented Actions Pluggable Agent Architecture Pluggable Agent Architecture

22. Situation (4) Spatial Situation –Has a region in the environment e.g.) ATM, Bus Stop, Bench, Ticket Booth, Crosswalk –The region is used for checking whether or not an agent is in the situation. Non-Spatial Situation –Social relationship between agents –Has no region in the environment –Directly set on crowds. e.g.) Friendship, Group member

23. Situation(5) Situation architecture Actions Sensors Behavior Functions Behavior Functions Event Rules Event Rules Walk Turn Sit Don’t’ turn Don’t overlap Path plan Empty sensor Empty sensor Proximity sensor Proximity sensor Signal sensor Signal sensor If(Empty) then Compose(Sitdown) If(Signal) then Compose(walk) If(Empty) then Compose(Sitdown) If(Signal) then Compose(walk)

24. Situation B Situation(6) Situation Composition –Union of all components of situations Situation A Composed Situation Situation C Agent can react to the situation A, B and C at the same time

25. Situation(7) Example Crossing to the other side of The road Traffic sign Crossing a street with checking traffic signs

26. Situation(8) Advantages of situation based simulation –Scalability Situation controls a small set of local behaviors. Agents keep only information of the situations that they are in at any given time. Situations can be composed/decomposed easily. –Ease of authoring –Re-usability –Efficiency

27. Situation Based Approach Scalability –Situation based simulation Convincingness –STM(Snap-Together-Motion) –Composable behaviors Controllability –Painting interface –Situation graphs

28. STM(Snap-Together-Motion) For visual convincingness, we use STM technique for animating characters. –From input motion clips, the STM produces a set of small motions that can be connected with each other with minimizing artifacts. [Gleicher et al. I3D 2003]

29. Composable Behaviors For semantically convincing behaviors, we propose the composable behavior technique based on the probability scheme. Agent A1 A2 A3 Probability Default Actions Action from a situation Actions

30. Composable Behaviors (2) Probability Scheme –Behavior functions compute the probability of each action based on its own criteria. –Returned probability distributions are composed by multiplication operation. –A sampling is performed on the final probability distribution result to select a final action.

31. Overlap Behavior Function Target Finding Behavior Function.5.3.7.6.19.43.37 Composed Prob. Dist Re-normalization Actions P(action) Collision with Other agents Agent has a Target pos. ABC ABC ABC ABC Multiplication

32. Composable Behaviors (4) 01.43 (B).19 (A).37 ( C) Sampling (0-1).19.43.37 Composed Prob. Dist Actions ABC Action selection through sampling

33. Composable Behaviors (5) Advantages –Gives a basic framework for scalability and controllability demands. –Provides randomness on simulation –Takes various kinds of factors into account for behaviors.

34. Situation Based Approach Scalability –Situation based simulation Convincingness –STM(Snap-Together-Motion) –Composable behaviors Controllability –Painting interface –Situation graphs (future work)

35. Painting Interfaces How to specify a particular situation in the environment. Spatial Situation Non-spatial Situation

36. Putting Pieces Together Preprocessing Create an environment Set situations Put crowds in the environment Simulation time Set run time situations Situations Plug-in information to agents Checking events with sensors Behavior composition Sampling on final prob. dist

37. Demos 1. Composable behaviors 2. Street environment 3. Theater environment 4. On-line situation setting 5. Painting interface 6. Visualization of crowds

38. Performance

39. Future Works (1) Smarter Situations –Problem Crowd flow planning –Solution Situation Graph –Represents aggregative relation between situations –Makes crowd follow a scenario –Provides interactive control –Controls the number of agents in a situation.

40. Situation Graph Ticket booth (start) Ticket booth (start) Gather and Talk Restroom Movie room (end) 100 50 10 70 Example

41. Future works (2) Hierarchical Situations –Problem Need to organize situations efficiently –Solution Hierarchical situations –Organizes situations in a hierarchical way »e.g.) parent (queue), child (Vertical queue, Horizontal queue)

42. Future Works (3) Hierarchical Environments –Problem Not easy to make a massive environment –Solution Hierarchical environment Town Theater Lobby Bench Once we make a theater environment, we can copy and paste it to wherever we want.

43. Future Works (4) Adjustment of Discrete Action Choices –Problem Failed in satisfying constraints because of shortage of discrete choices –Solution Provides a way to adjust actions to satisfy constraints –e.g.) If an agent has a target position, we can adjust the action choices to make agent move to the exact spot.

44. Future Works in Timeline Adjustment Of Action Choices Hierarchical Situation Hierarchical Environment Situation Graph Jun/04 Sep/04 Dec/04 Mar/05

45. Thanks Financial support : NSF, MIC of Korea Motion donations : House of Moves, Demian Gordon, Ohio State Unviersity Intellectual and technical support : M. Gleicher, S. Chenney, H.J. Shin, L. Kovar and all graphics group members