situation based approach for virtual crowd simulation ph.d preliminary talk mankyu sung

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

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Situation Based Approach forVirtual Crowd Simulation

Ph.D Preliminary talk

Mankyu Sung

Crowds

CrowdsIn

DifferentEnvironments

Sports event Street

Museum

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

Applications of Crowd Simulation

• Training

• Education

• Entertainment

• Architecture

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

Talk Outline

1. Research Goal

2. Related Works

3. Works to Date

4. Demo

5. Future Plan

The Goal of Research

• Set three demands that are able to solve these problems.

Scalability

Controllability

Convincingness

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.

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.

Controllability

• Specify crowd behaviors– User interfaces

• Control crowd flow– Predefined scenario– Interactive control– Density control

Proposed Approach

• Scalability– Situation based simulation

• Convincingness– STM(Snap-Together-Motion)– Composable behaviors

• Controllability– Painting interface– Situation graphs

(Sung et al. EG2004)

SituationBased

Approach

Thesis Statement

It is my thesis that the situation based approach is able to achieve the demand of

scalability, convincingness and controllability.

Related Works

• Smart Environments– Smart Object (Kallman et al. 1998)– Informed environment (Farenc et al. 1999)– Informed hierarchical information (Thomas et al. 200

0)– Apply Gibson’s “natural movement” theory (Michael et

al. 2003)

• Computer Games– The Sims TM (EA games)

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)

Related works

A1 A2 A3

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

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)

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 200

0)• Leader-Following model (Li et al. 2001)

Situation Based Approach

• Scalability–Situation based simulation

• Convincingness– STM(Snap-Together-Motion)– Composable behaviors

• Controllability– Painting interface– Situation graphs

SituationsSituation

A1 A2

A3A8

Agent

Behavior 1

Behavior 2

Character

Actions

A1 A2

A3

A4

A8

A7

A6 A5

Behavior 1

Behavior 2

Behavior 3...

Situations (2)• Example

A man

Actions

sing walk

turn

sit

climb

dance

stand cross

Zig-Zag walk

Straight walk

Sit down...

At a crosswalk

A man

crossstreet

stand

Straight walk

Checking cars

Situation

Situations (3)

AgentA1

A2

Behavior 1Behavior 1

Behavior 2Behavior 2

A3

A4

Behavior 3Behavior 3

Behavior 4Behavior 4

Behavior 5Behavior 5

AugmentedBehaviors

AugmentedActions

PluggableAgent

Architecture

PluggableAgent

Architecture

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

Situation(5)

• Situation architecture

ActionsActions SensorsSensors

BehaviorFunctions

BehaviorFunctions

Event Rules

Event Rules

WalkWalk

TurnTurn

SitSit

Don’t’ turnDon’t’ turn

Don’t overlapDon’t overlap

Path planPath plan

Empty sensor

Empty sensor

Proximity sensor

Proximity sensor

Signalsensor

Signalsensor

If(Empty) then

Compose(Sitdown)

If(Signal) then

Compose(walk)

If(Empty) then

Compose(Sitdown)

If(Signal) then

Compose(walk)

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

Situation(7)• Example

Crossing to the other side of

The road

Traffic sign

Crossing a streetwith

checking traffic signs

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

Situation Based Approach

• Scalability– Situation based simulation

• Convincingness– STM(Snap-Together-Motion)– Composable behaviors

• Controllability– Painting interface– Situation graphs

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]

Composable Behaviors• For semantically convincing behaviors, we propo

se the composable behavior technique based on the probability scheme.

Agent

A1

A2

A3

Probability

Probability

Probability

Default Actions

Action from a situation

Actions

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.

OverlapBehavior Function

Target FindingBehavior Function

.5 .5 .5

.3

.7.6

.19

.43.37

Composed Prob. Dist

Re-normalization

Actions

Actions

Actions

P(action)

P(action)

P(action)

Collision withOther agents

Agent has aTarget pos.

A B C

A B C

A B C

A B C

Multiplication

Composable Behaviors (4)

0 1

.43 (B).19 (A) .37 ( C)

Sampling (0-1)

.19

.43.37

Composed Prob. Dist

Actions

A B C

Action selection through sampling

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.

Situation Based Approach

• Scalability– Situation based simulation

• Convincingness– STM(Snap-Together-Motion)– Composable behaviors

• Controllability–Painting interface–Situation graphs (future work)

Painting Interfaces• How to specify a

particular situation in the environment.

SpatialSituation

Non-spatialSituation

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

Demos

• 1. Composable behaviors

• 2. Street environment

• 3. Theater environment

• 4. On-line situation setting

• 5. Painting interface

• 6. Visualization of crowds

Performance

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.

Situation Graph

Ticket booth(start)

Ticket booth(start)

Gather andTalk

Restroom

Movie room(end)

100

50

10

70

• Example

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)

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.

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.

Future Works in Timeline

AdjustmentOf

Action Choices

HierarchicalSituation

HierarchicalEnvironment

SituationGraph

Jun/04

Sep/04

Dec/04

Mar/05

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