Physical Based
Animation/Simulation
Particle Systems
Particle systems offer a solution to modeling
amorphous, dynamic and fluid objects like
clouds, smoke, water, explosions and fire.
Representing Objects with Particles
• An object is represented as clouds of
primitive particles that define its volume
rather than by polygons or patches that
define its boundary.
• A particle system is dynamic, particles
changing form and moving with the passage
of time.
• Object is not deterministic, its shape and form
are not completely specified. Instead
Basic Model of Particle Systems
1) New particles are generated into the system.
2) Each new particle is assigned its individual
attributes.
3) Any particles that have existed past their
prescribed lifetime are extinguished.
4) The remaining particles are moved and
transformed according to their dynamic
attributes.
5) An image of the particles is rendered in the
frame buffer, often using special purpose
algorithms.
Particle Attributes
• Initial position
• Initial velocity
• Initial size
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InitialSize = MeanSize + Rand() X VarSize
Initial color
Initial transparency
Shape
Lifetime
Alias|Wavefront’s Maya
Particle Dynamics
A particle’s position is found by simply adding its velocity
vector to its position vector. This can be modified by
forces such as gravity.
Other attributes can
vary over time as
well, such as color,
transparency and
size. These rates of
change can be global
or they can be
stochastic for each
particle.
Particle Extinction
• When generated, given a
lifetime in frames.
• Lifetime decremented
each frame, particle is
killed when it reaches
zero.
• Kill particles that no
longer contribute to
image (transparency
below a certain threshold,
etc.).
Particle Rendering
• Particles can obscure other objects behind them, can be
transparent, and can cast shadows on other objects. The
objects may be polygons, curved surfaces, or other
particles.
Star Trek II: The Wrath of Khan
Particle Hierarchy
Particle system such that particles can
themselves be particle systems.
The child particle systems can inherit the
properties of the parents.
Grass
• Entire trajectory of a particle
over its lifespan is rendered to
produce a static image.
• Green and dark green colors
assigned to the particles which
are shaded on the basis of the
scene’s light sources.
• Each particle becomes a blade
of grass.
white.sand by Alvy Ray Smith
(he was also working at Lucasfilm)
Soft Bodies
• Particle system
deforms the surface
of a NURBS or
polygonal object.
chewing gum soft body
Physical Based
Animation/Simulation
Flocking
• Schooling or swarming or herding
• Relate to groups of characters
• Craig W. Reynolds, “Flocks, herds and schools: A distributed
behavioral model”, SIGGRAPH 87
• Three simple rules (steering behavior):
– Separation, Alignment, Cohesion
Birds plus -oids
– Together gives groups of autonomous agents (boids) a realistic
form of group behavior similar to flocks of birds, schools of fish,
or swarms of bees. ex1, ex2
– The steering behavior determines how a character reacts to
other characters in its local neighborhood.
Emergent Behaviors
• Combination of three flocking rules results
in emergence of fluid group movements
• Emergent behavior
– Behaviors that aren’t explicitly programmed
into individual agent rules
• Ants, bees, schooling fishes
Three Rules (Steering Behaviors)
1. Separation: steer to avoid crowding
local flockmates
2. Alignment: steer toward the average
heading of local flockmates
3. Cohesion: steer to move toward the
average position of local flockmates
Three Rules (Steering Behaviors)
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In each rule, the steering behavior
determines how a character reacts
to other characters in its local
neighborhood.
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Characters outside of the local
neighborhood are ignored.
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The neighborhood is specified by a
distance which defines when two
characters are “nearby”, and an
angle which defines the character’s
perceptual “field of view.”
Separation
steer to avoid crowding local flockmates
Gives a character the ability to maintain a
certain separation distance from others nearby.
How to Compute Steering for Separation?
• First a search is made to find other
characters within the specified neighborhood
(exhaustive, spatial partitioning, caching
scheme)
• For each nearby character, a repulsive force
is computed by subtracting the positions of
our character and the nearby character,
normalizing, and then applying a 1/r
weighting. (That is, the position offset vector
is scaled by 1/r 2.)
• These repulsive forces for each nearby
character are summed together to produce
the overall steering force.
Alignment
steer toward the average heading of local flockmates
Gives an character the ability to align itself with (that
is, head in the same direction and/or speed as) other
nearby characters
How to Compute Steering for Alignment?
• Find all characters in the local neighborhood
(as described for separation)
• Average together the velocity (or alternately,
the unit forward vector) of the nearby
characters.
• This average is the “desired velocity,” and so
the steering vector is the difference between
the average and our character’s current
velocity (or alternately, its unit forward
vector).
• This steering will tend to turn our character
so it is aligned with its neighbors.
Cohesion
steer to move toward the average position of local flockmates
Gives an character the ability to cohere with
(approach and form a group with) other nearby
characters
How to Compute Steering for Cohesion?
• Find all characters in the local neighborhood
(as described for separation)
• Computing the “average position” (or “center
of gravity”) of the nearby characters.
• The steering force can applied in the
direction of that “average position”
(subtracting our character position from the
average position, as in the original boids
model), or it can be used as the target for
seek steering behavior.
Separation, Alignment and Cohesion
• In some applications it is sufficient to simply sum up the
three steering force vectors to produce a single combined
steering for flocking
• However for better control it is helpful to:
1. normalize the three steering components
2. scale them by three weighting factors before summing them.
• As a result, boid flocking behavior is specified by nine
numerical parameters:
– a weight (for combining),
– a distance and an angle (to define the neighborhood)
for each of the three component behaviors.
Combined Behaviors and Groups
• Flocking
(combining: separation, alignment, cohesion)
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Crowd Path Following
Leader Following
Unaligned Collision Avoidance
Queuing
(at a doorway)
Physical Based
Animation/Simulation
Cognitive Modeling
Use AI to allow for planning and learning
Control Algorithms
Simplified control loop
User
Control
Simulation
Frame
Use feedback to maintain:
 balance
 velocity (speed and direction)
 etc.
State Machines
Separate the motion or behavior into several
simple states
Simple states allow us to generate laws
State transitions are triggered by events
Example: fall forward until foot hits the ground
Running State Machine
Overview
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Virtual Creatures
Creature Representation
Creature Control
Physical Simulation
Behavior
Evolution
Results
Virtual Creatures
• Complexity vs. Control
• Genetic Algorithms
– Darwin (fitness)
– Differs from previous work
Creature Representation
• Genotype
• Phenotype
Creature Representation
• Directed Graph
– Nodes
• Information
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Dimensions
Joint-type
Joint-limits
Recursive-limit
Neurons
Connections
» Child Node
» Position
» Orientation
Creature Control
• Brain
– A directed graph of “neurons”
• Effectors
– Applied at Joints as Forces or Torques
• Muscle Pairs
Creature Control
• Neurons
– Provide different functions
• Sum, product, abs, max, sin, cos, oscillators, etc…
– Output vs. Input
• Number of inputs dependant on function
• Output dependant on input and maybe previous
state
Combining Control and
Representation
Physical Simulation
• Collision Detection
– Bounding Box + Pair Specific
• Collision Response
– Impulses + penalty springs
• Friction
• Viscosity
– For simulating underwater
Behavior
• Evolution for a specific behavior
– Swimming
– Walking
– Jumping
– Following (Land/Water)
• Fitness function evaluated at each step
– Weights for more preferred methods
Evolution
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Recipe for a successful evolution
1. Create initial genotypes
1. From scratch
2.
3.
4.
5.
Calculate survival ratio
Evaluate fitness and kill off the weaklings
Reproduce the most fit
Evolve, and proceed to step 3.
Evolution
Mating: CrossOver & Mutation
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Reproductive Method
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40% Asexual
30% Crossover
30% Grafting
Performance
• CM-5 with 32 processors – 3 Hours
– Population of 300
– 100 Generations
Results
• Homogeneity
• Swimmers
– Paddlers
– Tail-waggers
• Walkers
– Lizard-like
– Pushers/Pullers
– Hoppers
• Followers
– Steering Fins
– Paddlers
Overview of vBeluga
• Virtual belugas are shown in a wild pod context
• Incorporates research on beluga behavior and vocalization
conducted at aquarium UBC Zoology
• Flow: scientist – game – visitors : wild belugas : captive wild
• Simulation : AI architecture - belugas can learn and alter
their behavior based on changes in their environment –
updatable: new scientific thinking
• Physically-based system allows for natural whale
locomotion and realistic water – game research
• Realistic graphics : use of actuators (virtual bones and
muscles)
- game research
Beluga Behavior System
NNet, Action Selection
DiPaola,Akai,Kraus 06 "Experiencing Belugas: Developing an Action SelectionBased Aquarium Interactive", Journal of Adaptive Behavior Foundation AI
(NSERC)
DiPaola,Akai 06 “Designing Adaptive Multimedia Interactives to Support Shared
Learning Experiences", ACM Siggraph Education Design HCI / Informal Learning
(SAGE)
DiPaola,Akai 06 "Blending Science Knowledge and AI Gaming Techniques for
Experiential Learning", CA Game Studies Assoc. Gaming/Learning
DiPaola,Akai 05 “Shifting Boundaries: the Ontological Implications of Simulating
Marine Mammals”, NewForms, Museum of Anthropology IT/Society
Vancouver Aquarium: Adv.
Layer
Neural Net Layer
Flexibility of use
• Decouple Display with UI (tabletop)
– Control crowd by related placement
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Main gallery
Summer camp
Beluga encounters
Corporate gathering
full ui: tabletop, projection, signage
simple ui: on every system
guided system
main system, ambient mode
Physical Based
Animation/Simulation