Behavioral Animation

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Behavioral Animation

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Title: Behavioral Animation

1
Behavioral Animation

??????
??????????
3D??
??? Edward Shen
May 21, 2005

2
Course Information

Date 5/19, 5/21, 5/26, 5/28 (2005)
Lecturer Edward Yu-Te Shen ???
Course Website http//graphics.csie.ntu.edu.tw/e
dwards/dokuwiki/doku.php?idlecture

3
Lecturer

???, Edward Shen
PhD Candidate (1st year)
Graphics group, Dept. of CSIE, National Taiwan
University
http//graphics.csie.ntu.edu.tw/edwards/
edwards_at_cmlab.csie.ntu.edu.tw

4
Course Overview

This course course introduces several topics
related to behavioral animation, including
real-time crowd animation, collision detection
and bounding boxes, self-generating behaviors
through various techniques such as reinforcement
learning, genetic algorithms, and so on.
I hope that the class helps the students to gain
general knowledge in the related areas as well as
to apply such techniques in computer games and
animated films development. The four classes will
address more about design methodologies than
practical programming issues.

5
Schedule

Artificial Lives Fishes and Evolving Creatures
(Saturday, May 21)
Introduction to Artificial Lives
Artificial Fishes and Reinforcement Learned
Behavior
Evolving Creatures and Genetic Algorithm
Simulating Human Crowds (Thursday, May 26)
Collision Detection and Bounding Boxes (Saturday,
May 28)

6
Artificial Lives Fishes and Evolving Creatures

Introduction to Artificial Lives
Artificial Fishes and Reinforcement Learned
Behavior
Evolving Creatures and Genetic Algorithm

7
Applications
Source http//ligwww.epfl.ch/research/research_in
dex.html http//www.imdb.com/gallery/ss/0266543/F
NC-131.jpg http//www.lordoftherings.net/legend/g
allery/
8
Overview of Artificial Life / Behavioral
Animation

Artificial life modeling and the computer graphic
modeling hierarchy

Demetri Terzopoulos, Artificial life for computer
graphics, Communications of the ACM, v.42 n.8,
p.32-42
9
Overview of Artificial Life / Behavioral
Animation

Combine geometric models and kinematic models to
simplify key-framing
Mid 1980s Dynamic Simulation
particles
rigid bodies
deformable solids
fluids

10
Overview of Artificial Life / Behavioral
Animation

Biomechanical Modeling
Biological tissue
Internal muscles actuators
Behavior Modeling
Self-animating characters/ objects that react to
environment stimuli
Cognitive Modeling
Artificial intelligence
Knowledge representation
Reasoning
Planning

11
Examples of Behavioral Animation in the Movies
Demetri Terzopoulos, Artificial life for computer
graphics, Communications of the ACM, v.42 n.8,
p.32-42
12
Examples of Behavioral Animation in the Video
Games

Creatures (http//www.creaturs.co.uk)
The Sims (http// thesims.ea.com)
Demosaurus Rex (an experimental interactive game
environment)
etc

13
Artificial Life in Computer Graphics
Artificial plants
Conference proceedings cover image, SIGGRAPH '94
R. Mech, P. Prusinkiewicz, B. Wyvill, and A.
Glassner
14
Artificial Evolution
Hopping
Sims

Sims Sims 91,94
simulate evolution from genotypes to phenotypes
occasional mutations

15
Steering Behaviors
Action Selection strategy, goals, planning

Wandering
Seek / Flee
Pursuit / Evade
Path Following
Cohesion
Alignment
Separation
Leader Following

Steering path determination
Locomotion animation, articulation
16
Artificial Animals

Terzopoulos Tu and Terzopoulos 94
Basic behaviors
Simulation of fish movements using a simple
muscle system

17
Artificial Humans
HUMANOID-2

Thalmann Thalmann 98
Facial animation
Simulation of sensory perception
Interaction / communication between real humans
and virtual ones

18
Behavioral Animation

Goal Model animated characters / objects that
interact autonomously within a virtual world
Behavioral Animation shifts the traditional role
of the animator

We still probably want external control and
direction on an autonomous agent
Behavioral animation can be used as a vehicle for
setting up simulations of real world events

19
Behavioral Animation v.s. Key-framing

Key-framing
Animator Specifies the first and last frames of
the animation sequence
Computer Generates the in-between frames
Behavioral Animation
Animator Determines a set of rules for the
behavior of a character
Computer Animates the scene based on the rules
specified

20
Components of a Behavior Animation System

Framework of a generic Behavioral Animation
system

Millar et al. 99
21
Hanna et al. 2001
22
Perception Techniques

The perception techniques are the means by which
the animated character perceives its environment
Can be grouped roughly in 3 classifications
Zonal approach
Sensory approach
Synthetic vision approach

23
Zonal Approach

Objects in the zone are sensed by the character
Usually, perception zones are circular or
spherical but it can be otherwise

Perception Zone
24
Sensory Approach

There are areas where perception of an object is
vague and where it is precise
There might be many perception regions depending
on the senses involved (e.g. smell, touch, sight,
etc.)
Each object in the environment has a transmissive
area
Perception occurs when characters sensory area
intersects with the objects transmissive area

25
Behavioral Model

Decision making model of the system
Behavior can be
reactive
intelligent response
Form of response is various
movement vector
change in internal attributes
etc.
Different approaches used to implement the
behavioral techniques
Rules
Network
Artificial intelligence
Mathematical

26
Rule-Based Approach

Involves giving each animated character a set of
rules defining how they react to their
environment
() Can produce reasonable behaviors in a dynamic
environment
() Relatively easy to modify the rules to
produce different behaviors
(-) Reduce the chance of modeling behavior which
cannot be easily predicted
(-) Number of rules can grow very large in
complex environments
(-) Specific to a particular environment

27
Artificial Intelligence Approach

Artificial Intelligence Approach
Uses AI techniques such as reasoning engines and
neural networks
() Provides the ability to generate behaviors
which have not been explicitly programmed
(-) Requires a level of understanding by the
animator of the techniques being used

28
Mathematical Approach

The behavior of the character is defined in
mathematical terms
Ex Model of objects nature Model of
observers liking
Nature (N1, N2)
Liking (2, -3.7)
Model of function Attractiveness
Attractiveness N . L 2N1 3.7N2
() Provides a means of specifying behavioral
responses in a precise manner
(-) Not very intuitive for animators

29
Methods of Motion Generation

Traditional Principles (Keyframing)
Performance Capture (Motion Capture)
Modeling/Simulation (Physics, Behaviors)
Automatic Discovery (High-Level Control)

30
Keyframing (I)

Specify the key positions for the objects to be
animated.
Interpolate to determines the position of
in-between frames.

31
Keyframing (II)

Advantages
Relatively easy to use
Providing low-level control
Problems
Tedious and slow
Requiring the animator to understand the intimate
details about the animated objects and the
creativity to express their behavior in key-frames

32
Motion Interpolation

Interpolate using mathematical functions
Linear
Hermite
Bezier
and many others (see Richard Parents online
book)
Forward inverse kinematics for articulation
Specifying representing deformation

33
Basic Terminologies

Kinematics study of motion independent of
underlying forces
Degrees of freedom (DoF) the number of
independent position variables needed to specify
motions
State Vector vector space of all possible
configurations of an articulated figure. In
general, the dimensions of state vector is equal
to the DoF of the articulated figure.

34
Forward vs. Inverse Kinematics

Forward kinematics motion of all joints is
explicitly specified
Inverse kinematics given the position of the
end effector, find the position and orientation
of all joints in a hierarchy of linkages also
called goal-directed motion.

35
Forward Kinematics

As DoF increases, there are more transformation
to control and thus become more complicated to
control the motion.
Motion capture can simplify the process for
well-defined motions and pre-determined tasks.

36
Inverse Kinematics

As DoF increases, the solution to the problem may
become undefined and the system is said to be
redundant. By adding more constraints reduces
the dimensions of the solution.
Its simple to use, when it works. But, it gives
less control.
Some common problems
Existence of solutions
Multiple solutions
Methods used

37
Motion Capture (I)

Use special sensors (trackers) to record the
motion of a performer
Recorded data is then used to generate motion for
an animated character (figure)

38
Motion Capture (II)

Advantages
Ease of generating realistic motions
Problems
Not easy to accurately measure motions
Difficult to scale or adjust the recorded
motions to fit the size of the animated
characters
Limited capturing technology devices
Sensor noise due to magnetic/metal trackers
Restricted motion due to wires cables
Limited working volume

39
Basic Behavioral Component Loop
after Hanna et al. 2001

Execute all the mental triggers for each entity
Ex. Increase hunger level
Execute all the rules for each entity
Enter associated goals in entitys goal queue
Ex. Go to Wander Feed Mate
Select highest priority goal from queue and
execute it
Delete all expired goals
Display state of the animation

40
Coffee Break
41
Artificial Lives Fishes and Evolving Creatures

Introduction to Artificial Lives
Artificial Fishes and Reinforcement Learned
Behavior
Introduction
Physics-Based Fish Model Locomotion
Sensory Perception
Behavioral Modeling Animation
Intention Generator
Artificial Fish Types
Evolving Creatures and Genetic Algorithm

42
Go Fish!
43
Overview
Artificial Fish
Habits
Intention Generator
intention
Behavior Routines

F O C U S S E R
Filtered sensory data

Sensory data
Sensors

Control parameters
Behavior
Perception
Motor Controllers

Actuators(Muscles) Underlying Physical Model
Motor (Action)
3D Virtual World
44
Spring-Mass Dynamic Fish Model
45
Mechanics

node i have mass mi, position xi(t) xi(t),
yi(t), zi(t), velocity vi(t) dxi/dt,
acceleration ai(t) d2xi/dt2.
spring Sij connect node i to node j.
Denote springs constant as cij natural, rest
length as lij, eij(t) rij -lij, where rij
xj(t) xi(t)
The Lagrange equations of motion of the dynamics
fish

i 0, , 22,
where wi(t) ? j?Ni
46
Muscles Hydrodynamics

The artificial fish moves as a real fish does, by
contracting its muscles.
Characteristic of swinging tail contracting one
side, relaxing the other side.

When the fishs tail swings, it sets in motion a
volume of water. The inertia of the displaced
water produces a reaction force normal to the
fishs body and proportional to the volume of
water displaced per unit time, which propels the
fish forward.

With tail swinging towards positive X axis,
reaction force at point ni acts along the
inward normal. Component resists the
lateral movement, while is forward thrust.
Aggregate thrust propels fish towards positive Y
axis.

47
Motor Controllers

A artificial fish has three MCs, swim-MC,
left-turn-MC right-turn-MC.
The MCs prescribe muscle contractions to the
mechanical model. The swim-MC controls the
swimming segment muscle, while the turning MCs
control the turning segment muscles.
swim-MC(speed) r1, s1, r2, s2
Turn-MC(angle) r1, s1, r2, s2

48
Pectoral Fins

The pectoral fins control pitching and yawing.
The pectoral fins work by applying reaction force
to nodes in the midsection, i.e. nodes 1 ? i ?
12.
The pectoral fins are analogous to the wings of
an airplane. Pitch, yaw, and roll control stems
from changing their orientations relative to the
body i. e., the angle p/4 ? ? ? p.
A fin has an area A, surface n and the fish in
velocity v relative to the water, the fin force

49
Learning How to Swim

Eu Guiding the muscle actuator control function
u(t) by discouraging large, rapid flutuation of u
Ev Guiding good trajectory of the artificial
fish (final distance from a target location,
closeness to a specified speed, etc)

50
Learning Autonomously
51
Sensory Perception

The artificial fish has a vision sensor and a
temperature sensor.
The temperature sensor samples the surrounding
water temperature at the center of the artificial
fishs body.
The vision sensor has access to the geometry,
material property, and illumination information.
It can also interrogate the object database to
identify nearby objects and interrogate the
physical simulation to obtain information.

52
Behavioral Modeling Animation

At each time step, the intention generator issues
an intention based on the fishs habits, mental
state, and incoming sensory.
Habits a set of parameters that determine fish
habits.
Mental State function
H(t) min1 ne(t)R(?tH)/? , 1
L(t) mins(?tL)(1-H(t)), 1
F(t) min?Fi, 1, where Fi minD0/di(t), 1
H(t) hunger function
L(t) libido function
F(t) fear function
t time

53
Mental State Function (Hunger)

H(t) min1 ne(t)R(?tH)/? , 1
ne(t) amount of food consumed as measured
by the number of food particles or prey
fishes eaten.
R(x) R(x) 1-p0x with constant p0 is the
digestion rate
?tH the time since the last meal
? a constant that dictates the appetite of
the fish

54
Mental State Function (Libido)

L(t) mins(?tL)(1-H(t)), 1
s(x) s(x) p1x with constant p1 is the libido
function
?tL the time since the last mating

55
Mental State Function (Fear)

F(t) min?Fi, 1,
where Fi minD0/di(t), 1
D0 constant, D0 100
Fi the fear to sighted predator i
di the distance to sighted predator i

56
Intention Generator
Collision detection Danger of collision? Yes No

It avoid
Predator detection F gt f0 ?
If It-1 ? avoid Push the memory
No
Yes
Fm lt f1 ? And likes schooling? No Yes
Pop the memory Empty?
Yes
No
No
It escape
It school
IS eat or mate?
Yes
Generate new intention It by checking the mental
state and the habit string
Go to the focuser
Go to the next layer
57
Behavior Routines

Avoiding-static-obstacle
Avoiding-fish
Eating-food
Mating
Leaving
Wandering
Escaping
Schooling

58
Schooling Behavior Routine
gt 2 neighbours within one body length to the
front?
No
yes
find the closest schoolmate in front and speed up
towards it
gt 2 neighbours within one body length to the
sides?
No
yes
find the closest schoolmate to the sides and turn
to it
swim in generally the same direction as the
neighbours?
No
yes
make appropriate turns to match the general
orientation of the neighbours
Swim at the standard speed
59
Predators
Collision detection Danger of collision? Yes No

It avoid
Pop the memory Empty?
If It-1 ? avoid Push the memory
Yes
No
Hungry? Yes No
No
IS eat?
Yes
It eat
It wander
Go to the focuser
Go to the next layer
60
Predators

Chasing cost function of a prey k
Ck dk(1 ß1Sk ß2Ek /p)
Ck chasing cost
dk the distance between the mouth of the
predator and the center of prey ks body
Sk Sk1 if prey k is in a school of fishes,
otherwise Sk0
Ek the turning cost
ß parameters that tune the contributions of Sk
and Ek

61
Prey

The intention generator of a prey fish is a
specialization of the generic intention generator
as follows

Hungry?
Yes
No
It eat
likes schooling? or Is school?
Generate new intention It by checking the mental
state and the habit string
No
Yes
It wander
It school