Paper Summaries Any takers? Character Animation Projects - - PDF document

1

Character Animation

Paper Summaries

• Any takers?

Projects

• Presentations

– Schedule is now up on Web site – Please e-mail me with your preference of presentation day/time

• First come, first served

– All slots now taken…thanks.

Assignment #1

• All grades

– Comments / grades via e-mail – Doing “real time” in realtime.

Assignment 2/3

• Assignment 2: On queue to be graded

– Reminder: grace period ends tonight

• Assignment 3

– Due Feb 11th

Plan for today

• Articulated Figure Motion

– Intro / Forward Kinematics / Constraints – Inverse Kinematics – Walking – Advanced Algorithms – Today: Character Animation

• Levels of control / Motion capture systems

2

Motivation Films

• Animations by Chris Landreth

– Alias / Wavefront ( since 1994 )

Motivational Film

• The End (1995)

– Nominated for 1996 Academy Award for best animated short.

Motivational Film

• Bingo (1998)

– Test case for Maya

Plan For Today

• Topics

– Motion Capture Data – Levels of Control

Role of Animation

• Degrees of freedom

– Number of parameters whose values must be defined in order to fully position the articulated figure

Purpose of animation

Provide values to each of the DOF

for each time step.

Motion Capture

• The idea between motion capture

– You want realistic human motion?

• Go to the source
• No, not Newton this time…
• Use an actual human

– Disney has used this approach as far back as Snow White

• Using video as a guide
• Rotoscoping – tracing recorded video frames as basis for

animation

3

Motion Capture

• What motion capture gives us:

– Sampled values for each DOF in time.

• Since captured directly from human motion

– Subtleties of motion come for free. – Difficult for an animator to keyframe these subtleties

Motion Capture

Watt/Policarpo

Motion Capture

• Types of motion capture systems

– Optical

• Incorporate directionally-reflective balls referred to

as markers which attach to the performer.

• Three (at least) video cameras that track markers.
• Provides most flexibility for performers.
• Problem: Markers may be occluded from cameras

views.

Motion Capture

• Types of motion capture systems

– Prosthetic

• set of armatures attached all over the performer’s

body

• The armatures are connected to each other by using

a series of rotational and linear encoders.

• Accurate, though cumbersome for the performer

Motion Capture Systems

• Types of motion capture systems

– Acoustic

• An array of audio transmitters are strapped to

various parts of the performers body.

• Three receivers are triangulated to provide a point in

3D space.

• No occlusion problem.
• Cables are cumbersome to performers
• Ambient sound may interfere

Motion Capture Systems

• Types of motion capture systems

– Magnetic

• Much like acoustic except magnetic

transmitters/receivers used instead of acoustic

• No occlusion problem.
• Cables are cumbersome to performers
• Metal / other magnetic fields may interfere.

4

Motion capture Systems

• Challenges:

– Signal is not perfect

• Noisy
• missing data
• not perfectly aligned with joints

– Retargeting

• Data is only valid for virtual character who

possesses same scale as real character.

Motion Capture Systems

• Challenges:

– Even if motion capture data was perfect, we still have the following challenges:

• Re-use – use the motion for a slightly different purpose
• Creating impossible motion – Motion capture won’t do it, but

may be desired in animation

• Change of intent – we can’t always predict what motion we

will need

– Take Home Message: Motion Capture captures a particular, single motion.

Motion Capture Systems

• Examples

– From Eurographics Computer Animation and Simulation EGCAS'96

Motion Capture Data

• So what CAN we do with motion capture

data?

– We can

• speed up
• slow down
• time warp
• Motion warp

– However, one must remember that Captured data is Sampled Data.

Sampling Theory

• Signal - function that conveys information

– Audio signal (1D - function of time) – Image (2D - function of space)

• Continuous vs. Discrete

– Continuous - defined for all values in range – Discrete - defined for a set of discrete points in range.

Sampling Theory

• Point Sampling

– start with continuous signal – calculate values of signal at discrete, evenly spaced points (sampling) – convert back to continuous signal for display or

• utput (reconstruction)

5

Sampling Theory

Foley/VanDam

Sampling Theory

• Sampling can be described as creating a set
• f values representing a function evaluated

at evenly spaced samples n i i f fn , , 2 , 1 , ) ( K = ∆ =

∆ = interval between samples = range / n.

1 2

…

n

Sampling Theory

• Sampling Rate = number of samples per unit

∆ = 1 f

Sampling Theory

• Example -- CD Audio

– sampling rate of 44,100 samples/sec – ∆ = 1 sample every 2.26x10-5 seconds

Sampling Theory

• Rich mathematical foundation for sampling

theory

• Hope to give an “intuitive” notion of these

mathematical concepts

Sampling Theory

• Spatial vs frequency domains

– Most well behaved functions can be described as a sum of sin waves (possibly offset) at various frequencies – Describing a function by the contribution (and

• ffset) at each frequency is describing the

function in the frequency domain – Higher frequencies equate to greater detail

6

Sampling Theory

Foley/VanDam

Sampling Theory

• Nyquist Theorum

– A signal can be properly reconstructed if the signal is sampled at a frequency (rate) that is greater than twice the highest frequency component of the signal.

Sampling Theory

• Nyquist Theory

– Said another way, if you have a signal with highest frequency component at fh, you need at lease 2fh samples to represent this signal accurately.

Sampling Theory

• Example -- CD Audio

– sampling rate of 44,100 samples/sec – ∆ = 1 sample every 2.26x10-5 seconds

Sampling Theory

• Nyquist Theory -- examples

– CDs can accurately reproduce sounds with frequencies as high as 22,050 Hz.

Sampling Theory

• Aliasing

– Failure to follow the Nyquist Theorum results in aliasing. – Aliasing is when high frequency components of a signal appear as low frequency due to inadequate sampling.

7

Sampling Theory

• Aliasing - example

Foley/VanDam

Sampling Theory

• applet

Sampling Theory

• Anti-Aliasing

– What to do in an aliasing situation

• Increase your sampling rate (supersampling)
• Decrease the frequency range of your signal

(Filtering)

– How do we determine the contribution of each frequency on our signal?

Sampling Theory

• Fourier analysis

– Given f(x) we can generate a function F(u) which indicates how much contribution each frequency u has on the function f. – F(u) is the Fourier Transform – Fourier Transform has an inverse

Sampling Theory

• Fourier Transforms

Fourier Transform Inverse Fourier Transform f(x) F(u) f(x)

Sampling Theory

• How do we calculate the Fourier

Transform?

– Use Mathematics – For discrete functions, use the Fast Fourier Transform algorithm (FFT)

8

Sampling Theory

• Anti-Aliasing

– What to do in an aliasing situation

• Increase your sampling rate (supersampling)
• Decrease the frequency range of your signal

(Filtering)

– Since we already have the data sampled, we can’t supersample motion capture data – Thus, we need to filter

Sampling Theory

• Filtering -- Frequency domain

– Place function into frequency domain F(u) – simple multiplication with box filter S(u)

⎩ ⎨ ⎧ ≤ ≤ − = elsewhere , when , 1 ) ( k u k u S

Sampling Theory

• Filtering - frequency domain

Foley/VanDam

Sampling Theory

• Filtering -- Spatial Domain

– Convolution

∫

∞ ∞ −

− = ∗ = τ τ τ d x g f x g x f x h ) ( ) ( ) ( ) ( ) (

Taking a weighted average of the neighborhood around each point of f, weighted by g centered at that point.

Sampling Theory

• Convolution Applet

Sampling Theory

• Convolution and Filtering

– Convolution in the spatial domain is equivalent to multiplication in the frequency domain – Use Fourier Transform to convert filter from spatial to frequency & visa versa

9

Sampling Theory

• Convolving with a sinc function in the spatial

domain is the same as using a box filter in the frequency domain

Foley/VanDam

Sampling Theory

• Anti-aliasing -- Filtering

– Removes high component frequencies from a signal. – Removing high frequencies results in removing detail from the signal. – Can be done in the frequency or spatial domain

Sampling Theory

• Filtering - Convolution

Foley/VanDam

Motion capture data

• So what does all this mean w.r.t. motion capture

data?

– To avoid aliasing must filter before modifying data in time

• Motion capture sampling rates can be as high as 144 samples /

sec

– Filtering can also remove “noisy” data by removing high frequency components.

• Questions?
• Break!

Putting it all together

• We spent the last several weeks looking at

various aspects of articulated figure motion

• The 2nd half of this lecture is an attempt to

summarize into a single framework

– Added bonus: Intro to facial animation

Role of Animation

• Degrees of freedom

– Number of parameters whose values must be defined in order to fully position the articulated figure

Purpose of animation

Provide values to each of the DOF

for each time step.

10

Classification

• How does an animator do this?

– How much control? – How many parameters? – How smart are the characters?

Kinematic Control

• Lowest level of animator control

– Positions / orientations / velocities specified – Most control – Most parameters – Examples:

• Motion capture
• Keyframing

Procedural Control

• Kinematics are controlled via a procedure
• r set of rules.

– Less animator control – Parameter Set dependent upon rule set – Examples

• Dynamics – rules are physical
• Inverse Kinematics
• Behavioral Motion

Procedural Control

• Procedural Control can also be layered:

– Recall genetic motion

• Control programs determined parameters for

physical model

• Dynamical simulation determined kinematic

parameters

• Kinematic parameters defined motion

Action Level Control

• Motion can be organized into a number of

smaller actions (or gestures)

– Each action will involve one or more (but not necessarily all) of the model hierarchy – Each action will define the kinematics for the particular gesture

• Either directly or procedurally

– Animator control: specifies what gestures should be performed when

Task level control

• Higher level actions defined by scripts of a

set of gestures

– Example: walk

• May involve use of many gestures

– Task or goal driven

• Go from here to there

– Note:

• Can forgo the action level
• I.e. Flocking, genetic

11

Autonomous Control

• Character controls itself

– Knows set of task level scripts – “Decides” which to invoke and when – See AI for gaming course

Putting it together

Kinematic Procedural Actions Task Autonomous θ1 = 20, θ2 = 30, …. Foot=(x,y,z) – Hand=(x,y,z) Move foot, swing arms Walk to fridge, open door, get beer Joe, would you like a beer? Let’s get Joe a beer

Facial Animation

• As a means of example, consider facial

animation.

• Facial model:

– Human faces are non-trivial

• Most models consider both geometry and muscle
• Most models contain a number of low level control

points and low level control units

Facial Animation

• Facial model

– FACS (Facial Action Coding System) – Developed by Ekman and Freisen (pyschologists) – Consisted of action units that represented muscle groups

Facial Animation

• FACS

Facial Animation

• MPEG-4

– Like with the body model, MPEG-4 has a substantial facial model

• Facial Definition Parameters (FDP)

– Define control points on face – Real faces mapped to the FDPs

• Facial Animation Parameters (FAP)

– Used to control facial movement

12

Facial Animation

• MPEG-4

Kinematic control

• Specify lo-level parameter values for given

model.

• Note: Usually does not operate directly on

geometry.

– Facial model provides some level of abstraction.

Kinematic control

• Motion capture (Simgraphics)

Defining Actions

• Combine basic control units into actions or

gestures:

– Example:

• Eyebrow motion
• Mouth motion to emulate sounds

– Applet

Defining Tasks

• Combining gestures to perform tasks

– Examples

• Display emotion
• Speak words

Autonomous behavior

• Character determines:

– What to say – How to say it – What he/she/it is feeling

13

Putting it together

Kinematic Procedural Actions Task Autonomous AU1 = 20, AU2 = 30, …. Smile, say “G-et …” Be happy, Say “Get joe a beer” “Get your own beer” said angrily Say “Get Joe a beer” happily

Questions

• Next time:

– Sound and Animation