Adrian Treuille, Seth Cooper, Zoran Popović2006

Walter Kerrebijn045837607-06-2011

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 GraphCombined with reactive steering approach

Continuum Crowd Approach

Each person in a crowd:1. is trying to reach a goal2. moves at the maximum speed possible3. tries to avoid discomfortable areas4. 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 ApproachHypotheses

Maximum speed depends on:• environment• other people

Continuum Crowd ApproachHypotheses

Avoiding discomfort fields encourages people to take certain paths

Continuum Crowd ApproachHypotheses

Continuum Crowd ApproachHypotheses

Continuum Crowd ApproachOptimal Path

Continuum Crowd ApproachOptimal Path

Continuum Crowd ApproachOptimal Path

Calculating a potential field may be done simultaneously for a group of characters

Continuum Crowd ApproachSpeed

Speed is depending on:1. crowd density2. terrain

Continuum Crowd ApproachSpeed

Crowd Density Field

Average Velocity Field

Continuum Crowd ApproachSpeed

For areas of low crowd density, the speed is depending on the terrain

Continuum Crowd ApproachSpeed

For areas of high crowd density, the speed is depending on the crowd

Continuum Crowd ApproachSpeed

For areas of medium crowd density, the speed is depending on both the terrain and the crowd

Continuum Crowd ApproachPrediction

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

ImplementationDensity Conversion

ImplementationDynamic Field Construction

Choosing least cost neighbor:

Finite difference approximation:

Experiment

2D and 3D setups3.4 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