Adrian Treuille, Seth Cooper, Zoran Popović 2006 Walter Kerrebijn

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

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

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

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

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

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

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

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

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

Continuum Crowd Approach Hypotheses

Continuum Crowd Approach Hypotheses

Continuum Crowd Approach Optimal Path

Continuum Crowd Approach Optimal Path

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

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

Continuum Crowd Approach Speed Crowd Density Field Average Velocity Field

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

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

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

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

Implementation The algorithm used is as follows: Algorithm

Implementation The algorithm used is as follows: Algorithm

Implementation Density Conversion

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

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

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

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

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

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?

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