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Final exam date Final exam date has been announced: Articulated Figures III Tuesday, February 27, 2007 2:45 - 4:45pm Motion Capture 70-1435 Projects Project Presentations: Mid-quarter report Dates: All checked

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Articulated Figures III

Motion Capture

Final exam date

 Final exam date has been announced:

 Tuesday, February 27, 2007  2:45 - 4:45pm  70-1435

Projects

 Presentations:

 Dates:  Week 9: Wed, Feb 14  Week 10: Mon, Feb 19  Finals Week: Tues, Feb 27 (2:45-4:45)  15 minutes / presentation  Schedule now on Web  Please send me choice of time/day  ONLY Week 9 SLOTS AVAILABLE!

Project

 Mid-quarter report

 All checked…  E-mail if not received

Assignments

Assignment 1 -- Framework

Most have been graded

Assignment 2 -- Keyframing

Most have been graded.

Assignment 3 -- Billiards

50% graded

Assignment 4 -- Group Motion

Due Feb 7th.

NOTE: Dropbox close dates have been fixed.

Plan for today

 Next 2 weeks: Articulated Figures

 Last Wed: Forward Kinematics  Today: Inverse Kinematics  Wednesday: Motion Capture  Monday: Advanced algorithms

 Then

 Wednesday: Character animation

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Thinking About Spring?

Applications in Virtual Reality (4003-590-09 / 4005-769-09)

Virtual Theatre

A distributed computer system whereby performers, stage crew, and audience can be in physically separate places yet share in the same live theatrical performance.

Components

Torque Gaming Engine

moCap devices

Game networking

Details

4003-590-09 / 4005-769-09

TR 2-4 (ICL6)

Please contact Joe Geigel (jmg@cs.rit.edu) for details

Thinking About Spring

Procedural Shading

The goal of this course is to introduce students to the architectures and mechanisms of procedural shading and to teach them how to use shaders effectively in creating stunning visual effects.

Components

Advanced RenderMan

Real time shaders (Cg)

Lecture + Lab/Studio

Actual shaders form artists specs.

Details

4003-590-01 / 4005-769-01

TR 4-6 (ICL6)

PRE-REQ: Computer Graphics II

Plan for today

 Next 2 weeks: Articulated Figures

 Today: Forward Kinematics  Monday: Inverse Kinematics  Wednesday: Motion Capture  Monday: Advanced algorithms

 Then

 Wednesday: Character animation

Motivation Films

 Early examples of motion capture

Motivational Film

 Brilliance (1985)

 It’s all Apple’s fault!  Robert Abel & Associates  first entirely computer generated TV ad  Debuted at Super Bowl XIX (1985)  Who said motion capture was a new

technology?

Motivational Film

 Don’t Touch Me (1989)

 Diana Walczak and Jeff

Kleiser

 Synthespian, (Synthetic

Thesbians)

 Dojo – First female

Synthespian

 http://www.kwcc.com/

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Plan For Today

 Topics

 Motion Capture  Assignment #5

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

Rotoscoping

Used to trace motion of live actors, frame by frame into an animation

Invented by Max Fleischer in 1916

First used in Koko the Clown cartoons

Used extensively by Disney in Snow White

Motion Capture in CG

 First introduced by Abel and Associates

for “Brilliance”

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

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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) digital video cameras that track markers.  Provides most flexibility for performers.  Problem: Markers may be occluded from cameras views.

Optical Motion Capture

 Motion Analysis Corp

 I Robot  Final Fantasy  Entirely motion capture  Polar Express  video

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

Prosthetic Motion Capture

 Gypsy 4

 By MetaMotion

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

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Motion Capture Systems

 Types of motion capture systems

 ElectroMagnetic

 Much like acoustic except magnetic

transmitters/receivers used instead of acoustic

 No occlusion problem.  Cables are cumbersome to performers  Though now wireless solutions are available  Metal / other magnetic fields may interfere.

Electromagnetic Motion Capture

 MotionStar 2

 Ascension

Technologies

Motion Capture Systems

 Types of motion capture systems

 Fiber Optic Sensors

 Flexible FO sensors strapped to various parts of

the performers body.

 Sensors can directly measure joint rotations  Used in conjunction with electromagentic

sensor for head and torso.

Fiber Optics Motion Capture

 Shapewrap II

 Measurand

Capturing Human Motion

 Minimal set of

recording points

Frey, et. al

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.

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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 The Polar Express

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 output (reconstruction)

Sampling Theory

Foley/VanDam

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Sampling Theory

 Sampling can be described as creating a

set of values representing a function evaluated at evenly spaced samples n i i f fn , , 2 , 1 , ) ( K =

  • =

Δ = 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  Δ = 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

  • ffset) at various frequencies

 Describing a function by the contribution

(and offset) at each frequency is describing the function in the frequency domain

 Higher frequencies equate to greater detail

Sampling Theory

Foley/VanDam

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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  Δ = 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.

Sampling Theory

 Aliasing - example Foley/VanDam

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Sampling Theory

 Annoying audio aliasing applet  Example of aliasing in animation.

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)

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

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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

  • =
  • =
  • 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

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

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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!

Retargeting Motion Capture Data

 In general, moCap data is useful for a

single articulated figure.

Retargeting Motion Capture Data

 An inverse kinematic problem

 Eg. Walking – want feet on floor.

 [Gleicher98] takes a spacetime

constraints approach.

Spacetime Constraints

 The problem turns into a constrained

  • ptimization problem

 Find values Sj that minimize R subject to Ci

(Sj) = 0

 Si = DOF and forces for all time steps  Ci = constraints  R = minimization criteria

 Given these, there are well known

numerical techniques to solve

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Spacetime Constraints and Retargeting

 Si = joint angles  Constraints:

 Joint constraints (elbows don’t bend

backwards)

 Environment constrains (must not go

through floor)

 Motion constraints (char must pick up box

at frame 50)

Spacetime Constraints and Retargeting

 Minimization criteria

 Minimize “noticable change” from original

data

 Minimize difference of angles from original

data

 Minimize high frequency content of

changes

Spacetime Constraints and Retargeting

 Video  Web site on motion retargetting:

 http://www.cs.wisc.edu/graphics/Gallery/R

etarget/

Motion capture data formats

 No “standard” moCap data format

 Defacto standards from motion capture

system manufacturers

 Must specify both structure of skeleton

as well as sampled data for each joint.

Motion capture data formats

 Popular formats

 Acclaim File Format

 .asf (Acclaim skeleton format)  .amc (Acclaim motion capture)

 Biovision

 .bva (BioVision animation)  .bvh (BioVision Hierarchical)

 C3D

 Independent Binary format with programmer support.  http://www.c3d.org

Acclaim

.asf file .amc file

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.bvh file Questions?