1. Adrian Treuille, Seth Cooper, Zoran Popović 2006 Walter Kerrebijn 0458376 07-06-2011

2. Introduction Crowd Motion: large groups common goals collision avoidance real-time natural

3. Introduction Agent-based approach pros: independent decisions different simulation parameters Agent-based approach contras: emergent realism from behavioral rules hard to ensure computationally expensive distinction between global and local path-planning

4. Introduction Proposal: “Real-time motion synthesis model for large crowds without agent-based dynamics”

5. Introduction Motion: per-particle energy minimization dynamic potential and velocity fields merge of local and global path-planning

6. Related Work Methods in Game Development: Grid-based Navigation Meshes Waypoint Graph Combined with reactive steering approach

7. Continuum Crowd Approach Each person in a crowd: 1. is trying to reach a goal 2. moves at the maximum speed possible 3. tries to avoid discomfortable areas 4. picks the path minimizing the weighted sum of 1. and 2. and 3. Hypotheses

8. Continuum Crowd Approach Static goals can for example be: go to specific address go to ‘west side’ of town Dynamic goals can for example be: follow specific person find (non-)empty theater seat explore unseen parts of environment Hypotheses

9. Continuum Crowd Approach Hypotheses Maximum speed depends on: environment other people

10. Continuum Crowd Approach Hypotheses Avoiding discomfort fields encourages people to take certain paths

11. Continuum Crowd Approach Hypotheses

12. Continuum Crowd Approach Hypotheses

13. Continuum Crowd Approach Optimal Path

14. Continuum Crowd Approach Optimal Path

15. Continuum Crowd Approach Optimal Path Calculating a potential field may be done simultaneously for a group of characters

16. Continuum Crowd Approach Speed Speed is depending on: 1. crowd density 2. terrain

17. Continuum Crowd Approach Speed Crowd Density Field Average Velocity Field

18. Continuum Crowd Approach Speed For areas of low crowd density, the speed is depending on the terrain

19. Continuum Crowd Approach Speed For areas of high crowd density, the speed is depending on the crowd

20. Continuum Crowd Approach Speed For areas of medium crowd density, the speed is depending on both the terrain and the crowd

21. Continuum Crowd Approach Prediction Some predictive measures are necessary to reduce unnatural behavior: Predictive Discomfort - adds future density to discomfort field - should deal with perpendicular crossing Expected Periodic Field Changes - calculates expected speed - should deal with situations like traffic lights and doors

22. Implementation The algorithm used is as follows: Algorithm

23. Implementation The algorithm used is as follows: Algorithm

24. Implementation Density Conversion

25. Implementation Dynamic Field Construction Choosing least cost neighbor: Finite difference approximation:

26. Experiment 2D and 3D setups 3.4 GHz Nvidia Quadro FX 3400 [Movie]

27. Results/Conclusion Simulation steps took between 2 and 5 fps (?) Human animations were too simple Vortices and lanes emerged Agent interaction was possible Minimum Distance Enforcement was necessary

28. Assessment  The idea to merge local and global path planning is nice, but is it really better? weird behavior at traffic lights collisions still happen discomfort does not behave ‘natural’ enough individual control is lost there is no apparent group identity/cohesion  Does this method more closely resemble human psychology and path planning? global and local goals wandering

29. Assessment  It is not clear how the grid size is chosen, or how its choice influences the system  It is not clear why there is a ‘hard cut’ between low, medium and high crowd density speed calculations  There is no real mention of goal selection

30. Assessment  The experimental setup was not merged with an agents approach (as mentioned in the paper), only compared against it, so there is no way to see agent interactions with continuum crowds  FPS is not a measure of time, so how to evaluate these experiments?

31. Assessment  The results did not include tables, graphs or any other data visualization