Transcript Cloth Simulation - UCSD Computer Graphics Lab

Cloth Simulation
CSE169: Computer Animation
Instructor: Steve Rotenberg
UCSD, Winter 2004
Cloth Simulation
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Cloth simulation has been an important topic in
computer animation since the early 1980’s
It has been extensively researched, and has
reached a point where it is *basically* a solved
problem
Today, we will look at a very basic method of
cloth simulation. It is relatively easy to
implement and can achieve good results. It will
also serve as an introduction to some more
advanced cloth simulation topics.
Cloth Simulation with Springs
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We will treat the cloth as a system of particles
interconnected with spring-dampers
Each spring-damper connects two particles, and
generates a force based on their positions and velocities
Each particle is also influenced by the force of gravity
With those three simple forces (gravity, spring, &
damping), we form the foundation of the cloth system
Then, we can add some fancier forces such as
aerodynamics, bending resistance, and collisions, plus
additional features such as plastic deformation and
tearing
Cloth Simulation
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• Particle
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Spring-damper
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Particle
r : position
v : velocity
a : acceleration
m : mass
p : momentum
f : force
r•
v
1
a f
m
f   fi
p  mv
Euler Integration
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Once we’ve computed all of the forces in the system, we
can use Newton’s Second Law (f=ma) to compute the
acceleration
1
an  fn
m
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Then, we use the acceleration to advance the simulation
forward by some time step Δt, using the simple Euler
integration scheme
v n 1  v n  a n t
rn 1  rn  v n 1t
Physics Simulation
General Physics Simulation:
1. Compute forces
2. Integrate motion
- Repeat
Cloth Simulation
1. Compute Forces
For each particle: Apply gravity
For each spring-damper: Compute & apply forces
For each triangle: Compute & apply aerodynamic forces
2. Integrate Motion
For each particle: Apply forward Euler integration
Uniform Gravity
f gravity  mg 0
g 0  0  9.8 0
m
2
s
Spring-Dampers
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The basic spring-damper connects
two particles and has three constants
defining its behavior
 Spring constant: ks
 Damping factor: kd
 Rest length: l0
r2
•
v2
•
r1
v1
Spring-Damper
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A simple spring-damper class might look like:
class SpringDamper {
float SpringConstant,DampingFactor;
float RestLength;
Particle *P1,*P2;
public:
void ComputeForce();
};
Spring-Dampers
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The basic linear spring force in one dimension
is:
f spring  k s x  k s l0  l 
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The linear damping force is:
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We can define a spring-damper by just adding
the two:
f damp  kd v  kd v1  v2 
f s d  ks l0  l   kd v1  v2 
Spring-Dampers
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To compute the forces in 3D:
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Turn 3D distances & velocities into 1D
Compute spring force in 1D
Turn 1D force back into 3D force
Spring-Damper Force
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We start by computing the unit length
vector e from r1 to r2
We can compute the distance l
between the two points in the process
r2
•
e*  r2  r1
l  e*
e*
e
l
•
r1
e
l
Spring-Dampers
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Next, we find the 1D velocities
r2
v2  e v 2 •
v1  e v1
•
r1
v2
e
v1
Spring-Dampers
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f 2  f1
Now, we can find the 1D force and
map it back into 3D
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f s  d  k s l0  l   k d v1  v2 
f1  f s  d e
f 2  f1
•
f1  f s d e
e
Aerodynamic Force
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In the last lecture, we defined a simple
aerodynamic drag force on an object as:
f aero
1
2
  v cd ae
2
v
e
v
ρ: density of the air (or water…)
cd: coefficient of drag for the object
a: cross sectional area of the object
e: unit vector in the opposite direction of the
velocity
Aerodynamic Force
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Today we will extend that to a simple flat surface
Instead of opposing the velocity, the force
pushes against the normal of the surface
1
2
f aero    v cd an
2
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Note: This is a major simplification of real
aerodynamic interactions, but it’s a good place
to start
Aerodynamic Force
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In order to compute the aerodynamic
forces, we need surfaces to apply it to
We will add some triangles to our
cloth definition, where each triangle
connects three particles
r1
r3
r2
Aerodynamic Force
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In order to compute our force:
1
2
f aero    v cd an
2
we will need find the velocity,
normal, and area of the triangle
(we can assume that ρ and cd
are constants)
r3
r1
r2
Aerodynamic Force
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For the velocity of the triangle, we
can use the average of the three
particle velocities
v1  v 2  v 3
v surface 
3
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v1
We actually want the relative
velocity, so we will then subtract
off the velocity of the air
v  v surface  v air
v3
v surface
v2
Aerodynamic Force
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The normal of the triangle is:
n
r2  r1  r3  r1 
r2  r1  r3  r1 
r3
n
r1
r2
Aerodynamic Force
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The area of the triangle is:
1
a0  r2  r1  r3  r1 
2
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But we really want the crosssectional area (the area
exposed to the air flow)
v n
a  a0
v
n
v
v
Aerodynamic Force
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As the final equation requires |v|2an, we can
reduce the math a little bit:
n*  r2  r1  r3  r1 
v an 
2
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v v  n *
2n*
Also, notice that:
v
n*

v
2
n*
2
n*
Aerodynamic Force
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The final aerodynamic force is assumed to
apply to the entire triangle
We can turn this into a force on each
particle by simply dividing by 3, and
splitting the total force between them
Bending Forces
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If we arrange our cloth springs
as they are in the picture, there
will be nothing preventing the
cloth from bending
This may be find for simulating
softer cloth, but for stiffer
materials, we may want some
resistance to bending
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Bending Forces
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A simple solution is to add more
springs, arranged in various
configurations, such as the one
in the picture
The spring constants and
damping factors of this layer
might need to be tuned
differently…
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Collisions
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We will talk about collision detection & response
in the next lecture…
In the mean time, here’s a very basic way to
collide with a y=y0 plane
If(r.y < y0) {
r.y= y0 - r.y;
v.y= - elasticity * v.y;
v.x= (1-friction) * v.x;
v.z= (1-friction) * v.z;
}
// cheezy
// cheezy
Plastic Deformation
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An elastic deformation will restore back to
its un-deformed state when all external
forces are removed (such as the
deformation in a spring, or in a rubber ball)
A plastic deformation is a permanent
adjustment of the material structure (such
as the buckling of metal)
Plastic Deformation
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We can add a simple plastic deformation rule to the
spring-dampers
We do so by modifying the rest length
Several possible rules can be used, but one simple way
is to start by defining an elastic limit and plastic limit
The elastic limit is the maximum deformation distance
allowed before a plastic deformation occurs
If the elastic limit is reached, the rest length of the spring
is adjusted so that meets the elastic limit
An additional plastic limit prevents the rest length from
deforming beyond some value
The plastic limit defines the maximum distance we are
allowed to move the rest length
Fracture & Tearing
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We can also allow springs to break
One way is to define a length (or percentage of rest
length) that will cause the spring to break
This can also be combined with the plastic deformation,
so that fracture occurs at the plastic limit
Another option is to base the breaking on the force of the
spring (this will include damping effects)
It’s real easy to break individual springs, but it may
require some real bookkeeping to update the cloth mesh
connectivity properly…
Ropes & Solids
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We can use this exact same scheme to
simulate ropes, solids, and similar objects
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System Stability
Conservation of Momentum
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As real springs apply equal and opposite forces
to two points, they obey conservation of
momentum
Our simple spring-damper implementation
should actually guarantee conservation of
momentum, due to the way we explicitly apply
the equal and opposite forces
(This assumes that everything says within
reasonable floating point ranges and we don’t
suffer from excessive round-off)
Conservation of Energy
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True linear springs also conserve energy, as the kinetic
energy of motion can be stored in the deformation
energy of the spring and later restored
The dampers, however are specifically intended to
remove kinetic energy from the system
Our simple implementation using Euler integration is not
guaranteed to conserve energy, as we never explicitly
deal with it as a quantity
Conservation of Energy
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If we formulate the equations correctly and take
small enough time steps, the system will
hopefully conserve energy approximately
In practice, we might see a gradual increase or
decrease in system energy over time
A gradual decrease of energy implies that the
system damps out and might eventually come to
rest. A gradual increase, however, it not so
nice…
Conservation of Energy
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There are particle schemes that conserve energy, and
other schemes that preserve momentum (and/or angular
momentum)
It’s possible to conserve all three, but it becomes
significantly more complicated
This is important in engineering applications, but less so
in entertainment applications
Also, as we usually want things to come to rest, we
explicitly put in some energy loss through controlled
damping
Still, we want to make sure that our integration scheme
is stable enough not to gain energy
Simulation Stability
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If the simulation ‘blows up’ due to artificial
energy gains, then it is said to be unstable
The basic Euler integration scheme is the
simplest, but can easily become unstable and
require very small time steps in order to produce
useful results
There are many other integration schemes that
improve this behavior
We will only briefly mention these now, but might
go over them in more detail in a future lecture
Integration
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There are many methods of numerical
integration. Some examples are:
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Explicit Euler
Implicit Euler
Midpoint (Leapfrog)
Crank-Nicolson
Runge-Kutta
Adams-Bashforth, Adams-Moulton
etc…
Two-Level Integration Methods
Explicit Euler:
 n1   n  f (tn ,  n )t
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Implicit Euler
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
n 1
   f (tn 1 , 
Midpoint (Leapfrog):
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n 1
   f (tn 1/ 2 , 

n 1
Crank-Nicolson:
n
n

n 1
)t
n 1/ 2
)t

1
   f (t n ,  n )  f (t n 1 ,  n 1 ) t
2
n
Multipoint Methods
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Multipoint methods fit a polynomial to several values in
time. Adams-Bashforth methods use only previous
values, while Adams-Moulton combine these with
implicitly computed future points.
Second order Adams-Bashforth:
t
n 1
n
  
3 f (t n ,  n )  f (t n 1 ,  n 1 )
2

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Third order Adams-Moulton:
t
n 1
n
  
5 f (t n 1 ,  n 1 )  8 f (t n ,  n )  f (t n 1 ,  n 1 )
12


Runge-Kutta Methods
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The Runge-Kutta integration methods compute the value
at step n+1 by computing several partial steps between
n and n+1 and then constructing a polynomial to get the
final value at n+1
Second order Runge-Kutta:
t

 
f (t n ,  n )
2
 n1   n  t  f (tn1/ 2 ,  n1/ 2 )
n 1/ 2
n
Cloth Stability
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To make our cloth stable, we should choose a better
integration scheme (such as adaptive time-step fourth
order Runge-Kutta)
It’s actually not quite as bad as it sounds
But, in the mean time, some other options include:
 Oversampling: For one 1/60 time step, update the
cloth several times at smaller time steps (say 10
times at 1/600), then draw once
 Tuning numbers: High spring constants and damping
factors will increase the instability. Lowering these will
help, but will also make the cloth look more like
rubber…
Advanced Cloth
Continuum Mechanics
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Real cloth simulation rarely uses springs
Instead, forces are generated based on the the
deformation of a triangular element
This way, one can properly account for internal forces
within the piece of cloth based on the theory of
continuum mechanics
The basic process is still very similar. Instead of looping
through springs computing forces, one loops through the
triangles and computes the forces
Continuum models account for various properties such
as elastic deformation, plastic deformation, bending
forces, anisotropy, and more
Collision Detection & Response
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Cloth colliding with rigid
objects is tricky
Cloth colliding with itself is
even trickier
There have been several
published papers on robust
cloth collision detection and
response methods
Integration
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Nobody uses forward Euler integration for
cloth in the real world
Modern systems use adaptive time steps,
high order interpolation, and implicit
integration schemes
Particle Systems
Particle Systems
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In computer animation, particle systems can be
used for a wide variety of purposes, and so the
rules governing their behavior may vary
A good understanding of physics is a great place
to start, but we shouldn’t always limit ourselves
to following them strictly
In addition to the physics of particle motion,
several other issues should be considered when
one uses particle systems in computer
animation
Particles
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In physics, a basic particle is defined by
it’s position, velocity, and mass
In computer animation, we may want to
add various other properties:
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Color
Size
Life span
Anything else we want…
Creation & Destruction
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The example system we showed at the
beginning had a fixed number of particles
In practice, we want to be able to create and
destroy particles on the fly
Often times, we have a particle system that
generates new particles at some rate
The new particles are given initial properties
according to some creation rule
Particles then exist for a finite length of time until
they are destroyed (based on some other rule)
Creation & Destruction
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This means that we need an efficient way of handling a
variable number of particles
For a realtime system, it’s usually a good idea to allocate
a fixed maximum number of particles in an array, and
then use a subset of those as active particles
When a new particle is created, it uses a slot at the end
of the array (cost: 1 integer increment)
When a particle is destroyed, the last particle in the array
is copied into its place (cost: 1 integer decrement & 1
particle copy)
For a high quality animation system where we’re not as
concerned about performance, we could just use a big
list or variable sized array
Creation Rules
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It’s convenient to have a ‘CreationRule’ as an
explicit class that contains information about
how new particles are initialized
This way, different creation rules can be used
within the same particle system
The creation rule would normally contain
information about initial positions, velocities,
colors, sizes, etc., and the variance on those
properties
A simple way to do creation rules is to store two
particles: mean & variance (or min & max)
Creation Rules
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In addition to mean and variance properties,
there may be a need to specify some geometry
about the particle source
For example, we could create particles at
various points (defined by an array of points), or
along lines, or even off of triangles
One useful effect is to create particles at a
random location on a triangle and give them an
initial velocity in the direction of the normal. With
this technique, we can emit particles off of
geometric objects
Destruction
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Particles can be destroyed according to various rules
A simple rule is to assign a limited life span to each
particle (usually, the life span is assigned when the
particle is created)
Each frame, it’s life span decreases until it gets to 0,
then the particle is destroyed
One can add any other rules as well
Sometimes, we can create new particles where an old
one is destroyed. The new particles can start with the
position & velocity of the old one, but then can add some
variance to the velocity. This is useful for doing fireworks
effects…
Randomness
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An important part of making particle
systems look good is the use of
randomness
Giving particle properties a good initial
random distribution can be very effective
Properties can be initialized using uniform
distributions, Gaussian distributions, or
any other function desired
Particle Rendering
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Particles can be rendered using various
techniques
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Points
Lines (from last position to current position)
Sprites (textured quad’s facing the camera)
Geometry (small objects…)
Or other approaches…
For the particle physics, we are assuming that a
particle has position but no orientation.
However, for rendering purposes, we could keep
track of a simple orientation and even add some
rotating motion, etc…