11 behavioural animation chapter 11
TRANSCRIPT
11-1Department of Computer Science and Engineering
11 Behavioural Animation
Chapter 11
Behavioural Animation
11-2Department of Computer Science and Engineering
11 Behavioural Animation
Behavioral Animation
Knowing the environment
Aggregate behavior
Primitive behavior
Intelligent behavior
Crowd management
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Behavioral Animation
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Knowing the environment
Vision – what do you know about the present
Memory – what is recorded about the environment
More about AI than graphics
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Vision
Geometric issue – what’s in sight?
OR
Can I see X?
Perceptual issue – what do you see?
Computation v. accuracy
Cognitive modeling – necessary? At what level?
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Omniscience
Everything in database is ‘known’
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FOV Vision
Use surrogate bounding volumes, or sample points
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Occluded Vision
ray casting
sample environment
z-buffer
use object IDs as color
Use surrogate bounding volumes
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Target-testing vision
Can I see X?
Cast ray
Use surrogate bounding volumes
Sample object
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Object Recognition
Cognitive modeling
How much and what part is needed?
Application need?
Not yet addressed in literature
More AI than graphics
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Other senses?
Hearing?
Smell?
Model sensors & signal propagation
Spatial occupancy approach?
Applications?
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Memory
What is recorded about the environment
Transience of objects: time-stamps
Spatial occupancy
hierarchy: short-term, long-term
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Spatial Occupancy
doorway
doorway
transiency
wal
l
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Aggregate Behavior:E pluribus unum
Emergent Behavior
Type Elements Physics
Env/Others
Intelligence
Particles 102-104 Much/none None
Flocking 101-103 Some/some Limited
Crowds 101-102 Little/much Little-much
Typical qualities
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Primitive Behavior - Flocking
Local control – for realism, the flock member only reacts to
locally accessible information
Perception – FOV vision – angle can change with speed
Interacting with other members – stay with friends, avoid
bumping into each other
Interacting with the environment – collision avoidance is
primary
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Primitive Behavior - Flocking
Global control – need control of flock
script flock leader
global migratory urge
Negotiating the motion
Collision avoidance – steer to avoid
Splitting and rejoining – difficult to tune parameters
Modeling flight – e.g., banking into turns
Original work by Craig Reynolds
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Negotiating the MotionForces
Or
“Reasoning”
(e.g. rule-based)
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11 Behavioural AnimationNavigating Obstacles
Problems with repulsive forces
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Navigating using bounding sphere
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NavigatingTesting for being on a collision path with (bounding) sphere
rt
kst
PCs
V
VPCk
22
)(
Given: P, V, C, r
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Finding closest non-colliding point
sWUtPCPB
UVU
UVUW
PC
PCU
trs
PC
PCrkt
ttPCPCtrk
tPCsk
tsr
rPCk
)(
2
2
)(
22
222
22222
222
222
22
Calculate s,t
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Navigating – finding a pass
To avoid collision: find closest point on edge to pass object
Vision Options:
Render in z-buffer
Sample environments with rays
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Modeling Flight –common in flocking
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Modeling Flight
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Modeling Flight
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Modeling Flight
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Primitive Behavior - Prey-Predator
unbalanced abilities
vision - distance, movement, fov
maximum velocity
maximum acceleration
maximum angular velocity maximum
angular acceleration
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Prey-Predator - vision
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Prey-Predator agility: speed and turning
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Prey-Predator – hidden by forces
Using pure forces
May not prevent object penetration
Prey can be ‘hidden’ by environmental repulsive forces
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Intelligent Behavior
Autonomous behavior
‘Self-animated’ characters
Perception & reasoning about environment
Personality, emotions, dispositions
Manifestations of Individuality
Body Expressions and Gestures
Facial expressions
Speech
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Internal State
Suggested precedence classes of internal state variables
Imperatives
Desires
Suggestions
Models what the agent needs to do
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Levels of Behavior
Hooks for the
animator to
impose control
Arbitration
among
competing
intentions
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Expressions and Gestures
BEAT
EMOTE
RUTH
Greta
ToBI – Tones and Break Indices
LMA – Laban Movement Analysis
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EMOTE
Expressive MOTion Engine (EMOTE)
Speech driven movement and gestures
Torso & arms
Laban Movement Analysis
Effort
Shape
N. Badler at U.Penn
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BEAT
Input text – marked up
Generate non-verbal
behavior in sync with speech
Facial expressions,
head & body motions
gestures
J. Cassell at M.I.T.
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ruth
http://www.cs.rutgers.edu/~village/ruth/
Doug DeCalro at Rutgers
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Facial Expression
http://expression.sourceforge.net/
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Facial Expression
Ken Perlin: http://mrl.nyu.edu/~perlin/
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SmartBody
https://www.youtube.com/watch?v=sD7Q777wIaQ
More videos: https://smartbody.ict.usc.edu/video
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Modeling Personality
Personality – long term qualities
Emotions – short term
Mood – third level
Basic emotions: happy, sad, fear, disgust, surprise, anger
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Personality ModelsBiology/evolutionary approaches
brain & anatomy
biochemistry & personality
genetics & personality
Psychoanalytic approach
psychometrics
Freud, Adler, Jung
Adaptation Theory:
traits: passive, aggressive, withdrawn
Need theory: Freud + experiential learning
Arousal Theory: absorb & discharge energy
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Type & Trait Theory
Type: individuals slotted into a type
e.g. introvert v. extrovert
Trait: lie on gradation
combination defines behavior in situation
e.g.,
Internal states: how perceptions dictate emotional
experience
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Trait Theory
OCEAN: openness, conscientiousness, extroversion,
agreeableness, neuroticism
PEN: extraversion, neuroticism, psychoticism
OCC: how perceptions dictate emotional experience
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Modeling Individuality
Improv
AlphaWolf
11-46Department of Computer Science and Engineering
11 Behavioural AnimationDominant personality
AlphaWolf: http://alumni.media.mit.edu/~badger/alphaWolf.html
Simulation of a pack of wolves and their behavior
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Crowd Management
Emergent behavior
Statistical behavior v. believable individual behaviors
Homogeneous activity v. Internal structure
For evaluation
Pedestrian traffic simulation
Traffic flow
Emergency response modeling
For entertainment
Background crowds
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Crowds
Emergent behavior:
similar to flocking
collision avoidance
‘intelligent’ paths
From a distance: statistical behavior
nonsensical detailed motion
reasonable visual effect
Internal structure
limited interaction among members
group formation
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Crowd Applications
For evaluation
Building evacuation, e.g. virtual fire drill
Architecture evaluation, e.g. signage
For training
Military scenarios, e.g. sniper training
Emergency response, e.g. disaster response
For entertainment: e.g., background crowds
games
films, e.g., Titanic, Saving Private Ryan,
Lord of the Rings
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Qualities of crowd
Individual processing – amount of computation per member
Physics – simulated reaction to environment
Intelligence - reasoning capability - agents
Emergent behavior - similar to flocking, flocking system
Uniform – sameness of members
Quantity & density - average distance between members
Viewing distance – aggregate behavior, inspect individuals
Function – simple traversal, background activity, main actions
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Uniformity, granularity
Individuality:
Believable activity at level of individual
Background noise:
Activity without intention
Statistical behavior:
On average, intentional activity
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Execution environment
Real-time v. Off-line computation
simple computations
avoid n-squared algorithms
size limited
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Spatial organization
Cellular decomposition:
Regular 2D grid
Adjacency accessible
Density limited
Cells define obstructions
Continuous space:
Step in any direction
Need to decipher obstructions
Perception needed
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Perception Modeling
Vision
Memory
Knowledge of environment
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Navigation
Rule-based
Fluid flow:
density fields, potential functions
Cognitive modeling
Flocking systems:
individual perception, navigation
Particle systems:
Individual navigation
Cellular automata
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Panic & Congestion handling
Personal space
Packing people during evacuation
Stairwell traversal
Exit awareness
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Motion & Navigation
Potential fields
Path planning
Passing on pathways
Roadmaps
Forming & maintaining subgroups
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Structure in crowds
A collection of Individuals – personality
modeling
Homogenous – no individuality
Subgroups
Group by belief systems
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Penn Station
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Other topics
Comparison to real-world situations
Heterogeneous – pedestrians and cars
Data driven crowds – image processing
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Massivehttp://www.massivesoftware.com/ Commercial de facto standard
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Examples
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Examples
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Examples