Oct 16, Fall 2006 IAT 410 1

Animation

Low-Level behaviors OverviewKeyframingMotion CaptureSimulation

Oct 16, Fall 2006 IAT 410 2

Low-Level Behaviors

Keyframing Motion Capture Simulation

Oct 16, Fall 2006 IAT 410 3

Generating Motion

What matters?– Quality of motion appropriate for

rendering style and frame rate– Controllable from UI– Controllable from AI– Personality of the animated character

Oct 16, Fall 2006 IAT 410 4

Keyframe Example

Oct 16, Fall 2006 IAT 410 5

Keyframing

Fine level of control Quality of motion depends on skill of

animator

Oct 16, Fall 2006 IAT 410 6

Motion Capture

Natural-looking motion Hard to generalize motions

– Registration is difficult– “Weightless” according to professional

animators

Oct 16, Fall 2006 IAT 410 7

Motion CaptureImages courtesy Microsoft Motion Capture Group

Oct 16, Fall 2006 IAT 410 8

Simulation (Broadly Defined)

Physics is hard to simulate Pseudo-physics is somewhat hard Control is very hard Gives Generalization + Interactivity

User/AI

DesiredBehavior

Control

Forces and Torques Model

Numerical Integrator

Graphics

State

Oct 16, Fall 2006 IAT 410 9

When to Use What Method? Keyframing

– Sprites and other simple animations– Non-human characters– Coarse collision detection

Motion Capture– Human figures– Subtle motions, long motions

Simulation– Passive simulations– When interactivity w/ motion is

important

Oct 16, Fall 2006 IAT 410 10

Keyframing

Oct 16, Fall 2006 IAT 410 11

Keyframing

Oct 16, Fall 2006 IAT 410 12

Keyframe Example

Oct 16, Fall 2006 IAT 410 13

Keyframing

Fine level of control Quality of motion depends on skill of

animator

Oct 16, Fall 2006 IAT 410 14

Hand Drawn Animation -- 2D Sketches Pencil tests Inking Coloring Digitize to

sprites

Oct 16, Fall 2006 IAT 410 15

Computer Animation: 2D or 3D

Sketches Models and

materials Key configurations Playback of

motion or render to sprites

Oct 16, Fall 2006 IAT 410 16

Keyframing

The development process:Adjust trajectoryPlayback motion

Parameters:– Locations– Joint angles– Shape -- flexible objects– Material properties

– Camera Motion– Lighting

Oct 16, Fall 2006 IAT 410 17

Keyframing Interpolation

Inbetweening

1,2,3 4 5 6 7,8,9…Linear

v

1,2,3 4 5 6 7,8,9…Slow in, Slow out

v

time

time

Oct 16, Fall 2006 IAT 410 18

Interpolation Example

Oct 16, Fall 2006 IAT 410 19

Key Frames1

2

3

Oct 16, Fall 2006 IAT 410 20

Key Frames

Timeline 1 2 3 3

1

Oct 16, Fall 2006 IAT 410 21

Inbetweening

Frame dependent (“wrong”) slow-in/out– Iterate once per frame

– This is a variant on the Infinite Impulse Response (IIR) filter:

1,))1((

weightweight

xweightxx finalpreviousnew

10

),1()(

weight

weightxweightxx finalpreviousnew

Oct 16, Fall 2006 IAT 410 22

Inbetweening

Frame-Independent (“right”) slow in/out

– Compute acceleration a

221

00 attvxx

20 )(2

middle

middle

t

xxa

fatvv 0

vtxx f 0

Time Tendft Time of last frame

Oct 16, Fall 2006 IAT 410 23

Spline-driven Animation

x

x,y = Q(u)for u:[0,1]

Equal arc lengths Equal spacing in u

y

Oct 16, Fall 2006 IAT 410 24

Reparameterize Arc Length

S= A(u) = arc length Reparam: Find:

Bisection search for a valueof u where A(u) = S with anumerical evaluation of A(u)(Details in Watt & Watt)

))(()( 1 sAQuQ )(1 sAu

Oct 16, Fall 2006 IAT 410 25

Keyframing -- Constraints

Joint limits Position limits Inverse kinematics

Oct 16, Fall 2006 IAT 410 26

Keyframing -- Constraints

Oct 16, Fall 2006 IAT 410 27

Coordinate Systems

Oct 16, Fall 2006 IAT 410 28

Kinematics The study of motion without regard

to the forces that cause it

Draw graphics Specify fewer Degrees Of Freedom (DOF)

More intuitive control of DOF Pull on hand Glue feet to ground

Oct 16, Fall 2006 IAT 410 29

Inverse Kinematics

Oct 16, Fall 2006 IAT 410 30

Inverse Kinematics

Oct 16, Fall 2006 IAT 410 31

What makes IK Hard?

Many DOF -- non-linear transcendental equations

Redundancies– Choose a solution that is “closest” to

the current configuration– Move outermost links the most– Energy minimization– Minimum time

Oct 16, Fall 2006 IAT 410 32

IK Difficulties

Singularities– Equations are ill-conditioned near

singularities– High state-space velocities for low

Cartesian velocities Goal of “Natural Looking”

motion– Minimize jerk (3rd derivative)

Oct 16, Fall 2006 IAT 410 33

Motion Capture

What do we need to know?– X, Y, Z– Roll, Pitch, Yaw

Errors cause– Joints to come apart– Links grow/shrink– Bad contact points

Sampling Rate and Accuracy

Oct 16, Fall 2006 IAT 410 34

Motion Capture

Goals:– Realistic motion– Lots of different motions (300-1000)– Contact

Appropriate game genres– Sports– Fighting– Human characters

Oct 16, Fall 2006 IAT 410 35

ApplicationsMovies, TVVideo gamesPerformance animation

Oct 16, Fall 2006 IAT 410 36

Oct 16, Fall 2006 IAT 410 37

Oct 16, Fall 2006 IAT 410 38

Plan out Shoots Carefully

Know needed actions (80-100 takes/day)– Bridges between actions– Speed of actions– Starting/ending positions

Hire the right actor– Watch for idiosyncrasies in motion– Good match in proportions

Oct 16, Fall 2006 IAT 410 39

Sensor Placement

Place markers carefully– Capture enough information– Watch for marker movement

Check data part way through shoot Videotape everything!

Oct 16, Fall 2006 IAT 410 40

Oct 16, Fall 2006 IAT 410 41

Technology

Numerous technologies Record energy transfer

– Light– Electromagnetism– Mechanical skeletons

Oct 16, Fall 2006 IAT 410 42

Technology Passive reflection – Peak Performance

Tech– Hand or semi-automatically digitized– Video– Time consuming

Issues– No glossy or reflective

materials– Tight clothing– Marker occlusion by props+High frames/sec

Oct 16, Fall 2006 IAT 410 43

Technology Passive reflection --Acclaim, Motion

Analysis– Automatically digitized– 240Hz– Not real-time, Correspondence– 3+ markers/body part– 2+ cameras for 3D position data

Oct 16, Fall 2006 IAT 410 44

Technology

Vicon Motion Systems– Retroreflective paint on

reflectors– Lights on camera– Very high contrast markers

Oct 16, Fall 2006 IAT 410 45

Technology

Active light sources -- Optotrak– Automatically digitized– 256 markers– 3500 marker/sec– Real-time– Specialized cameras

Oct 16, Fall 2006 IAT 410 46

Technology Electromagnetic Transducers

– Ascension Flock of Birds, etc– Polhemus Fastrak, etc

Limited range/resolution– Tethered (cables to box)– Metal in environment (treadmill,

Rebar!)– No identification problem– 6DOF Realtime– 30-144 Hz 13-18 markers

Oct 16, Fall 2006 IAT 410 47

Technology

Exoskeleton + angle sensors– Analogous

– Tethered– No identification problem– Realtime - 500Hz– No range limit - Fit– Rigid body approximation

Oct 16, Fall 2006 IAT 410 48

Technology

Dataglove– Low accuracy– Focused resolution

Monkey– High accuracy– High data rate– Not realistic

motion– No paid actor

Mechanical motion capture

Oct 16, Fall 2006 IAT 410 49

Technology

Technology issues– Resolution/range of motion– Calibration– Accuracy– Occlusion/Correspondence

Oct 16, Fall 2006 IAT 410 50

Animation Issues

Style Scaling Generalization

Oct 16, Fall 2006 IAT 410 51

Resolution

Positioning of camera

Oct 16, Fall 2006 IAT 410 52

Markers, Calibration Marker Placement

– Location should move rigidly with joint– Stay away from bulging muscles, loose skin– Shoulders: Skeletal motion not closely tied to skin

motion Calibration

– Zero position– Fine calibration by hand

Oct 16, Fall 2006 IAT 410 53

Calibration

Finding Joint Locations– Move markers to joint centers

• Assume rigid links, rotary joints

Shoulder?

Oct 16, Fall 2006 IAT 410 54

Calibration

Extract best limb lengths Use estimator to compute limb

length

Minimize or reject outliers

Oct 16, Fall 2006 IAT 410 55

Calibration

Example estimator:– 508 frames of walking– 6 bad frames– Collarbone to shoulder:

• Hand editing: 13.3cm• Estimator: 13.2cm• Arithmetic mean: 14.1cm

Oct 16, Fall 2006 IAT 410 56

Accuracy

Marker movement Noise in sensor readings Skew in measurement time Environment restrictions Frame rate

– High frame rate allows good filtering

Oct 16, Fall 2006 IAT 410 57

Camera Calibration

Internal camera parameters– Optical distortion of lens

External parameters– Position and orientation

Correlation between multiple cameras

Oct 16, Fall 2006 IAT 410 58

Model-Based Techniques

Restricted search space for markers

Dynamics (velocity integration) – No infinite accelerations

Model of behavior Model of bodies of occlusion

– Objects don’t pass through each other

Oct 16, Fall 2006 IAT 410 59

Scaling Animation Contact Movement style Inverse kinematics

Oct 16, Fall 2006 IAT 410 60

Generalizating Animation

Interpolation Synthesis for Articulated Figure Motion

Wiley and Hahn IEEE CG&A v17#6

Oct 16, Fall 2006 IAT 410 61

Generalizating Animation Keyframes as constraints in a smooth

deformation– Create functions by hand

that warp the joint angle curves through time

Keyframe placing the ballon the racket at impact

Motion WarpingWitkin and Popovic, SIGGRAPH’95

Oct 16, Fall 2006 IAT 410 62

Motion Warping

For each joint angle curve C(t) Cnew(t) = C(t) * A(t) + B(t) A(t) is usually a smooth, gentle curve

Oct 16, Fall 2006 IAT 410 63

Generalizating Animation Motion Editing With Spacetime

Constraints– Michael Gleicher– 1997 Symposium on Interactive 3D

Graphics

Oct 16, Fall 2006 IAT 410 64

Blending Animations Efficient Generation of Motion Transitions Using

Spacetime Constraints– Rose, Guenter, Bodenheimer, Cohen– Siggraph ’96– Uses dynamics to compute plausible paths– Blends these paths

Oct 16, Fall 2006 IAT 410 65

Simulation

Modeling the real world with simple physics– Realism– A set of rules– Better interactivity

Objects or Characters

Oct 16, Fall 2006 IAT 410 66

Passive -- No muscles or motors

Active -- Internal source of energy

Oct 16, Fall 2006 IAT 410 67

Equations of Motion

Water Explosions Rigid body models

Oct 16, Fall 2006 IAT 410 68

Control Systems

Wide variety of behaviors Transitions between behaviors Controllable by AI or UI Robust

Oct 16, Fall 2006 IAT 410 69

Equations of Motion

Oct 16, Fall 2006 IAT 410 70

Generating Motion

What matters?– Quality of motion appropriate for

rendering style and frame rate– Controllable from UI– Controllable from AI– Skills of the animated character– Personality of the animated character

Oct 16, Fall 2006 IAT 410 71

Keyframing

Fine level of control Quality of motion depends on skill of

animator

Oct 16, Fall 2006 IAT 410 72

Motion Capture

Natural-looking motion Hard to generalize motions

– Registration is difficult Often seems “weightless” – Bill Kroyer,

Rhythm & Hues

Oct 16, Fall 2006 IAT 410 73

Simulation (Broadly Defined)

Physics is hard to simulate Pseudo-physics is somewhat hard Control is very hard Gives Generalization + Interactivity

User/AI

DesiredBehavior

Control

Forces and Torques Model

Numerical Integrator

Graphics

State

Oct 16, Fall 2006 IAT 410 74

When to Use What Method? Keyframing

– Sprites and other simple animations– Non-human characters– Coarse collision detection

Motion Capture– Human figures– Subtle motions, long motions

Simulation– Passive simulations– When interactivity w/ motion is important

Oct 16, Fall 2006 IAT 410 75

Integration of Technologies

Layering– Add hand/finger motion later– Facial animation

Use keyframing to modify data– Fix holes in data

Use motion capture to drive simulation