Particle Systems Three - Computer Animation - Lecture Slides, Slides of Computer Graphics and Animation

DURING THE COURSE WORK OF MY MS, I LEARN ABOUT THE ANIMATION AND THIS LECTURE SLIDES OF THIS COURSE WORK OF "Computer Animation" HAVE THE IMPORTANT POINTS:Particle Systems Three, Computer Animation, Special Effects, Industry, Behavior, Relatively Simple, Lots of Particles, Non-Physical Rules, Physical, Exact Situation

Typology: Slides

2012/2013

Uploaded on 04/30/2013

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Forward Euler Integration
Note that this:
is very similar to the result we would get if we
just assumed that the particle is under a uniform
acceleration for the duration of one frame:
( )2
1tt nnnn Δ+Δ+=
+avrr
( )2
12
1tt nnnn Δ+Δ+=
+avrr
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Forward Euler Integration

 Note that this:

is very similar to the result we would get if we

just assumed that the particle is under a uniform

acceleration for the duration of one frame:

2 1

t t

n n n n

r r v a

2 1

t t

n n n n

r r v a

Forward Euler Integration  Actually, it will work either way  Both methods make assumptions about what happens in the finite time step between two instants, and both are just numerical approximations to reality  As _t approaches 0, the two methods become equivalent  At finite _t, however, they may have significant differences in their behavior, particularly in terms of accuracy over time and energy conservation  As a rule, the forward Euler method works better  In fact, there are lots of other ways we could approximate the integration to improve accuracy, stability, and efficiency

Forward Euler Integration  One area where the forward Euler method fails is when one has very tight springs  A small motion will result in a large force  Attempting to integrate this using large time steps may result in the system diverging (or ‘blowing up’)  Therefore, we must use lots of smaller time steps in order for our linear approximation to be accurate enough  This resorting to many small time steps is where the computationally simple Euler integration can actually be slower than a more complex integration scheme that costs more per iteration but requires fewer iterations  We will look at more sophisticated integration schemes in future lectures

Particle Systems

Particles  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:

 Color

 Size

 Life span

 Anything else we want…

Creation & Destruction

 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 Rules

 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

 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

Randomness  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

 Particles can be rendered using various

techniques

 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…