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Presented by: Yoram Atir Simon Adar. Animating (human) motion. Applications of computer animation. Movies Advertising Games Simulators …. General goals of the work presented. New methods aimed to save time/money/skills needed. - Study motion (texture). Agenda. - Basic concepts

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Presentation Transcript
applications of computer animation
Applications of computer animation
  • Movies
  • Advertising
  • Games
  • Simulators
general goals of the work presented
General goals of the work presented
  • New methods aimed to save time/money/skills needed.

- Study motion (texture).

agenda
Agenda

- Basic concepts

- Motion Synthesis/texture using motion capture

  • Physics/Biomechanics Motion Synthesis

- Cartoon Motion Retargeting.

basic concepts
Basic concepts
  • Animation world (3D)
  • Skeletal model representation
  • Model positioning
  • Keyframes
  • Motion capture
  • Frequency bands
  • Correlations

Basic Concepts

3d animation world
3D animation world
  • (Human) model is animated in Object space
  • Animated model projected into “global” space
  • Camera is placed and rotated
  • Perspective is set
  • Other…

Basic Concepts

skeletal representation
Skeletal representation
  • Each model has its own Default Pose
  • DOF’s – joint angles/translations relative to Default Pose
  • Hierarchical (tree) skeletal representation of model

Picture from Lecture in Computer Graphics course

Department of computer science

University of Washington

Basic Concepts

creating motion
Creating motion
  • Skeletal variations between frames
  • Overall rotation/Translation between frames
  • Correlate.

General Problem:

A LOT of work due to the large number of DOFS & high frame rate

Basic Concepts

figure positioning
Figure positioning
  • Forward kinematics (simplified): Figure positioning by joint data specification.

Problem:

  • Tedious trial and error.

Basic Concepts

figure positioning10
Figure positioning

Inverse kinematics (simplified)

  • Joint data is acquired by solving for the final position
  • In general, This is an optimization problem with a large system of variables and constraints
  • Problems often are expressed as minimization problems, and solved using standard algorithms (gradient decent etc).
  • Usually, infinite number of possible solutions.
  • A “good” solution has to be more than “feasible”
  • Often one is obtained by embedding specific knowledge as additional constraints, and/or
  • Using Inverse kinematics as a part of a specific solution.

Basic Concepts

basic methods for saving labor
Basic methods for saving labor

Motion capture

KeyFrames

Basic Concepts

keyframes
Keyframes
  • Specifying only part of DOFs and frames
  • Computer interpolation between them

Problem: “smooth” interpolation looks unreal

There are methods to apply “specific noise”

  • Term has historical roots

Basic Concepts

motion capture
Motion capture
  • Acquired from “live action”
  • Copied onto animated character
    • Problem: Hard to adapt.
    • “Motion Editing” – methods to adapt mocap
  • Done in studios
  • Mocap libraries exist

Basic Concepts

keyframing vs mocap
Keyframing vs. Mocap

Advantages

Disadvantages

  • Control

Keyframing

Mocap

Basic Concepts

keyframing vs mocap15
Keyframing vs. Mocap

Advantages

Disadvantages

  • Control
  • Intuitive

Keyframing

Mocap

Basic Concepts

keyframing vs mocap16
Keyframing vs. Mocap

Advantages

Disadvantages

  • Control
  • Intuitive
  • Detail hard

Keyframing

Mocap

Basic Concepts

keyframing vs mocap17
Keyframing vs. Mocap

Advantages

Disadvantages

  • Control
  • Intuitive
  • Detail hard
  • Many DOF

Keyframing

Mocap

Basic Concepts

keyframing vs mocap18
Keyframing vs. Mocap

Advantages

Disadvantages

  • Control
  • Intuitive
  • Detail hard
  • Many DOF

Keyframing

  • Detail easy

Mocap

Basic Concepts

keyframing vs mocap19
Keyframing vs. Mocap

Advantages

Disadvantages

  • Control
  • Intuitive
  • Detail hard
  • Many DOF

Keyframing

  • Detail easy
  • All DOF

Mocap

Basic Concepts

keyframing vs mocap20
Keyframing vs. Mocap

Advantages

Disadvantages

  • Control
  • Intuitive
  • Detail hard
  • Many DOF

Keyframing

  • Detail easy
  • All DOF
  • No control

Mocap

Basic Concepts

keyframing vs mocap21
Keyframing vs. Mocap

Advantages

Disadvantages

  • Control
  • Intuitive
  • Detail hard
  • Many DOF

Keyframing

  • Detail easy
  • All DOF
  • No control
  • Not intuitive

Mocap

Basic Concepts

frequency bands
Frequency Bands

Right flat

Right toe

Left flat

Left toe

Basic Concepts

frequency bands24
Frequency Bands
  • Simplifies the form of the data
    • Low frequency Variations: Large scale motions.
    • Higher frequency variations: individual “noise” / Jitter

Both are important to preserve in order to capture the essence of motion

Basic Concepts

correlations
Correlations
  • Joints angle/translation data is related to each other
  • Joint angles are correlated over time
  • Correlation “plot” is
    • (somewhat) Specific to the type of motion
    • Carries “personality” information (style)

Basic Concepts

more information
More information…

INTRODUCTION TO COMPUTER ANIMATION – Rick parent

http://www.cis.ohio-state.edu/~parent/book/outline.html

Splines

http://www.people.nnov.ru/fractal/splines/Intro.html

Hash Inc - Animation software (Movies, tutorials…)

http://www.hash.com

Google…

agenda27
Agenda

- Basic concepts

- Motion Synthesis/texture using motion capture

  • Physics/Biomechanics Motion Synthesis

- Cartoon Motion Retargeting

goal motion capture assisted animation
Goal: Motion Capture Assisted Animation
  • Create a method that allows an artist low-level control of the motion
  • Combine the strengths of keyframe animation with those of mocap

Motion Capture Assisted Animation – Pullen/Bregler

goal motion capture assisted animation29
Goal: Motion Capture Assisted Animation

“Sketch” an animation by keyframing

  • Animate only a few degrees of freedom
  • Set few keyframes

“Enhance” the result with mocap data

  • Synthesize missing degrees of freedom
  • Texture keyframed degrees of freedom

Motion Capture Assisted Animation – Pullen/Bregler

what is a motion texture
What is a Motion Texture?
  • Every individual’s movement is unique
  • Synthetic motion should capture the texture
  • To “texture” means to add style to a pre-existing motion
  • Technically, texturing is a special case of synthesis
goal motion capture assisted animation31
Goal: Motion Capture Assisted Animation

Blue = Keyframed

Purple = Textured/Synthesized

Motion Capture Assisted Animation – Pullen/Bregler

how an animator works
How an Animator Works
  • A few degrees of freedom at first
  • Not in detail
  • Fill in detail with more keyframes later

Motion Capture Assisted Animation – Pullen/Bregler

the method in words
The Method in Words
  • Choose degrees of freedom to drive the animation
  • Compare these degrees of freedom from the keyframed data to mocap
  • Find similar regions
  • Look at what the rest of the body is doing in those regions
  • Put that data onto the keyframed animation

Motion Capture Assisted Animation – Pullen/Bregler

choices the animator must make
Choices the Animator Must Make
  • Which DOF to use as matching angles
  • Which DOF to texture, which to synthesize
  • Which frequency band to use in matching
  • How many frequency bands to use in texturing
  • How many matches to keep
  • How many best paths to keep

Motion Capture Assisted Animation – Pullen/Bregler

before beginning choose matching angles
Before Beginning:Choose Matching Angles

Root x trans

Root y trans

Root z trans

Root x rot

Root y rot

Root z rot

Spine1 x

Spine1 y

Spine1 z

Spine2 x

Spine2 y

Spine2 z

Spine3 x

Spine3 y

Spine3 z

Neck x

Neck y

Neck z

Head x

Head y

Head z

Head Aim x

Head Aim y

Head Aim z

Left Clavicle x

Left Clavicle y

Left Clavicle z

Left Shoulder x

Left Shoulder y

Left Shoulder z

Left Elbow x

Left Elbow y

Left Elbow z

Left Wrist x

Left Wrist y

Left Wrist z

Right Clavicle x

Right Clavicle y

Right Clavicle z

Right Shoulder x

Right Shoulder y

Right Shoulder z

Right Elbow x

Right Elbow y

Right Elbow z

Right Wrist x

Right Wrist y

Right Wrist z

Left Hip x

Left Hip y

Left Hip z

Left Knee x

Left Knee y

Left Knee z

Left Ankle x

Left Ankle y

Left Ankle z

Left Ball x

Left Ball y

Left Ball z

Right Hip x

Right Hip y

Right Hip z

Right Knee x

Right Knee y

Right Knee z

Right Ankle x

Right Ankle y

Right Ankle z

Right Ball x

Right Ball y

Right Ball z

Time

Time

Time

matching angles drive the synthesis
Matching Angles Drive the Synthesis

Root x trans

Root y trans

Root z trans

Root x rot

Root y rot

Root z rot

Spine1 x

Spine1 y

Spine1 z

Spine2 x

Spine2 y

Spine2 z

Spine3 x

Spine3 y

Spine3 z

Neck x

Neck y

Neck z

Head x

Head y

Head z

Head Aim x

Head Aim y

Head Aim z

Left Clavicle x

Left Clavicle y

Left Clavicle z

Left Shoulder x

Left Shoulder y

Left Shoulder z

Left Elbow x

Left Elbow y

Left Elbow z

Left Wrist x

Left Wrist y

Left Wrist z

Right Clavicle x

Right Clavicle y

Right Clavicle z

Right Shoulder x

Right Shoulder y

Right Shoulder z

Right Elbow x

Right Elbow y

Right Elbow z

Right Wrist x

Right Wrist y

Right Wrist z

Left Hip x

Left Hip y

Left Hip z

Left Knee x

Left Knee y

Left Knee z

Left Ankle x

Left Ankle y

Left Ankle z

Left Ball x

Left Ball y

Left Ball z

Right Hip x

Right Hip y

Right Hip z

Right Knee x

Right Knee y

Right Knee z

Right Ankle x

Right Ankle y

Right Ankle z

Right Ball x

Right Ball y

Right Ball z

Time

Time

Time

motion capture data
Motion Capture Data

Root x trans

Root y trans

Root z trans

Root x rot

Root y rot

Root z rot

Spine1 x

Spine1 y

Spine1 z

Spine2 x

Spine2 y

Spine2 z

Spine3 x

Spine3 y

Spine3 z

Neck x

Neck y

Neck z

Head x

Head y

Head z

Head Aim x

Head Aim y

Head Aim z

Left Clavicle x

Left Clavicle y

Left Clavicle z

Left Shoulder x

Left Shoulder y

Left Shoulder z

Left Elbow x

Left Elbow y

Left Elbow z

Left Wrist x

Left Wrist y

Left Wrist z

Right Clavicle x

Right Clavicle y

Right Clavicle z

Right Shoulder x

Right Shoulder y

Right Shoulder z

Right Elbow x

Right Elbow y

Right Elbow z

Right Wrist x

Right Wrist y

Right Wrist z

Left Hip x

Left Hip y

Left Hip z

Left Knee x

Left Knee y

Left Knee z

Left Ankle x

Left Ankle y

Left Ankle z

Left Ball x

Left Ball y

Left Ball z

Right Hip x

Right Hip y

Right Hip z

Right Knee x

Right Knee y

Right Knee z

Right Ankle x

Right Ankle y

Right Ankle z

Right Ball x

Right Ball y

Right Ball z

Time

Time

Time

overview
Overview

Steps in texture/synthesis method

  • Frequency analysis
  • Matching
  • Path finding
  • Joining

Motion Capture Assisted Animation – Pullen/Bregler

example
Example

In the following series of slides:

Hip angle = matching angle

Spine angle = angle being synthesized

Motion Capture Assisted Animation – Pullen/Bregler

frequency analysis break into bands
Frequency Analysis:Break into Bands

Motion Capture Assisted Animation – Pullen/Bregler

frequency analysis
Frequency Analysis

Band-pass decomposition of matching angles

Keyframed Data

Motion Capture Data

Frequency

Time

Motion Capture Assisted Animation – Pullen/Bregler

frequency analysis42
Frequency Analysis

Chosen low frequency band

Keyframed Data

Motion Capture Data

Frequency

Time

Motion Capture Assisted Animation – Pullen/Bregler

chosen low frequency band
Chosen Low Frequency Band

Hip angle data (a matching angle)

Keyframed Data

Motion Capture Data

Motion Capture Assisted Animation – Pullen/Bregler

making fragments
Making Fragments

Break where first derivative changes sign

Keyframed Data

Motion Capture Data

Motion Capture Assisted Animation – Pullen/Bregler

making fragments45
Making Fragments

Step through fragments one by one

Keyframed Data

Motion Capture Data

Motion Capture Assisted Animation – Pullen/Bregler

matching
Matching

Keyframed

Fragment

Motion Capture Assisted Animation – Pullen/Bregler

matching47
Matching

Motion Capture Data

Keyframed

Fragment

Motion Capture Assisted Animation – Pullen/Bregler

matching48
Matching

Motion Capture Data

Keyframed

Fragment

Motion Capture Assisted Animation – Pullen/Bregler

matching49
Matching

Compare to all motion capture fragments

Angle in degrees

Keyframed

Mocap

Time

matching50
Matching

Resample mocap fragments to be same length

Angle in degrees

Keyframed

Mocap

Time

matching51
Matching

Using some metric on all matching anglesand on their first derivatives:

Keep the K closest matches

Angle in degrees

Keyframed

Mocap

Time

matching52
Matching

Motion Capture Data

Keyframed

Fragment

Motion Capture Assisted Animation – Pullen/Bregler

matching53
Matching

Motion Capture Data

Close

Matches

Keyframed

Fragment

Motion Capture Assisted Animation – Pullen/Bregler

matching54
Matching

Hip Angle (Matching Angle)

Spine Angle (For Synthesis)

Motion Capture Assisted Animation – Pullen/Bregler

matching and synthesis
Matching and Synthesis

Low frequency hip angle data (a matching angle)

Spine angle data to be synthesized

Motion Capture Assisted Animation – Pullen/Bregler

matching and synthesis56
Matching and Synthesis

Low frequency hip angle data (a matching angle)

Spine angle data to be synthesized

Motion Capture Assisted Animation – Pullen/Bregler

matching and synthesis57
Matching and Synthesis

Low frequency hip angle data (a matching angle)

Spine angle data to be synthesized

Motion Capture Assisted Animation – Pullen/Bregler

matching and synthesis58
Matching and Synthesis

Low frequency hip angle data (a matching angle)

Spine angle data to be synthesized

Motion Capture Assisted Animation – Pullen/Bregler

matching and synthesis59
Matching and Synthesis

Low frequency hip angle data (a matching angle)

Spine angle data to be synthesized

Motion Capture Assisted Animation – Pullen/Bregler

matching and synthesis60
Matching and Synthesis

Low frequency hip angle data (a matching angle)

Spine angle data to be synthesized

Motion Capture Assisted Animation – Pullen/Bregler

path finding
Path Finding
  • We would like to:
  • Use as much consecutive fragments as possible
  • Stay as close as possible to best fit

Angle in degrees

Time

path finding63
Path Finding

Angle in degrees

Time

path finding64
Path Finding

Angle in degrees

Time

path finding65
Path Finding

Angle in degrees

Time

path finding66
Path Finding

Angle in degrees

Time

joining
Joining

Angle in degrees

Time

enhancing animations texturing and synthesis
Enhancing Animations:Texturing and Synthesis

Not keyframed

Keyframed

Synthesized

Textured

texturing
Texturing

Synthesize upper frequency bands

Motion Capture Assisted Animation – Pullen/Bregler

texturing70
Texturing

Band-pass decomposition of keyframed data

Frequency

Time

texturing71
Texturing

Synthesize upper frequency bands

Frequency

Time

walking animations texturing and synthesis73
Walking animationsTexturing and Synthesis

Motion Capture Data

Two different styles of walk

walking animations texturing and synthesis74
Walking animationsTexturing and Synthesis

Enhanced Animation

Upper body is synthesized

Lower body is textured

otter animations texturing76
Otter Animations: Texturing

Textured animation

dance animations texturing and synthesis79
Dance Animations: Texturing and Synthesis

Enhanced Animation

Blue = Keyframed

Purple = Textured/Synthesized

dance animations texturing
Dance Animations: Texturing

Keyframed Sketch With More Detail

dance animations texturing81
Dance Animations: Texturing

Textured Animation

Blue = Keyframed

Purple = Textured

summary of the method
Summary of the Method

Sketch +

Mocap

Frequency

Analysis

Matching

Matching Angles

Keyframed data

Keyframed Data

Mocap Data

Mocap Data

Possible Synthetic Data

Path Finding

Joining

Enhanced

Animation

choices the animator must make83
Choices the Animator Must Make
  • Which DOF to use as matching angles
  • Which DOF to texture, which to synthesize
  • Which frequency band to use in matching
  • How many frequency bands to use in texturing
  • How many matches to keep
  • How many best paths to keep
conclusions and applications
Conclusions and Applications
  • Appropriate for an artist interested in a very particular style of motion
  • The artist may have a relatively small motion capture set of that style
  • The artist may want precise control over parts of the motion
conclusions and further work
Conclusions and Further Work
  • Direct incorporation of hard constraints
  • Fundamental units of motion
for more info
For more info. . .

http://graphics.stanford.edu/~pullen

Special Thanks to:

Reardon Steele, Electronic Arts

agenda87
Agenda

- Basic concepts

- Motion Synthesis/texture using motion capture

  • Physics, Biomechanics Motion Synthesis

- Cartoon Motion Retargeting

motivation
Motivation
  • Generate rapid prototyping of realistic character motion
  • Avoid simulated human models, that are very complex, and don’t always look realistic

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

scope
Scope
  • Highly dynamic movement such as jumping, kicking, running, and gymnastics.
  • Less energetic motions such as walking or reaching will not work well in this framework

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

overview of the process
Overview of the process

Motion sketch

Constraint & phase

detection

Character description

Motion DB

Transition pose

synthesis

Momentum control

User interaction

Objective functions

Optimization

Animation

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

overview of the process91
Overview of the process
  • The objective is to transforms simple animations into realistic character motion by applying laws of physics and the biomechanics domain
  • The unknowns are: values of joint angles and parameters of angular and linear momentum

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

overview of the process92
Overview of the process

Motion sketch

Constraint & phase

detection

Character description

Motion DB

Transition pose

synthesis

Momentum control

User interaction

Objective functions

Optimization

Animation

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

constraint and stage detection
Constraint and stage detection
  • Each input sequence has two parts:
    • The part that needs to be improved
    • The part that needs to kept intacked
  • Automatically extract the positional and sliding constrains

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

positional constraint detection
Positional constraint detection
  • A positional constraint fixes a specific point on the character to a stationary location for a period of time
  • We need to find if all these points lie on a line, plane
  • In an articulated character we find the constraints on each body part

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

positional constraint detection95
Positional constraint detection
  • The algorithm looks for fixed points (point, line, plane)

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

sliding constraints
Sliding constraints

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

overview of the process97
Overview of the process

Motion sketch

Constraint & phase

detection

Character description

Motion DB

Transition pose

synthesis

Momentum control

User interaction

Objective functions

Optimization

Animation

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

transition pose generation
Transition pose generation
  • A transition pose separates constrained and unconstrained stages.
  • Two possibilities:
    • We ask the animator to draw the transition poses
    • We have an estimator to suggest a transition pose

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

transition pose estimator
Transition Pose Estimator
  • DB contains examples of different motions
  • The input of that DB are the motionparameters like: flight distance, flight height, takeoff angle, landing angle, spin angle..
  • The DB has a simplified representation of the transition poses by three COM’s
  • We use IK to obtain the full character’s pose from those three COM’s
  • The KNN - K nearest neighbor algorithm
  • The pose estimator predicts the candidate pose by interpolating the KNN with the weights that describe the similarity to the input.

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

overview of the process100
Overview of the process

Motion sketch

Constraint & phase

detection

Character description

Motion DB

Transition pose

synthesis

Momentum control

User interaction

Objective functions

Optimization

Animation

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

momentum control
Momentum control
  • Transition poses constrain the motion at few key points of the animation
  • Dynamic constraints ensure realistic motion of each segment
  • Linear and angular momentum give us these dynamic constraints

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

momentum during unconstrained and constrained stages
Momentum during unconstrained and constrained stages
  • linear momentum - During “flight” the only force is gravity
  • Angular momentum - During “flight” there is no change in Angular momentum
  • During “ground” stage we avoid computing the momentums and use empirical characteristics

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

overview of the process103
Overview of the process

Motion sketch

Constraint & phase

detection

Character description

Motion DB

Transition pose

synthesis

Momentum control

User interaction

Objective functions

Optimization

Animation

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

objective functions
Objective functions
  • There are three Objective functions, the basic idea behind them is power consumption
    • Minimum mass displacement
    • Minimal velocity of DOFs
    • Static balance

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

overview of the process105
Overview of the process

Motion sketch

Constraint & phase

detection

Character description

Motion DB

Transition pose

synthesis

Momentum control

User interaction

Objective functions

Optimization

Animation

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

putting it all together
Putting it all together
  • Environment constraints (Ce)
  • Transition pose constraints (Cp)
  • Momentum constraints (Cm)
  • Q are character’s DOFs

subject to

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

overview of the process107
Overview of the process

Motion sketch

Constraint & phase

detection

Character description

Motion DB

Transition pose

synthesis

Momentum control

User interaction

Objective functions

Optimization

Animation

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

some results
Some Results
  • Wide variety of figures: male, female, child
  • 51 DOFs
  • The body dimensions and mass distribution is taken from biomechanics literature
  • In some of the cases the animator selects the body parts to be constraints
  • The animator can change relative timing between each phase
  • The optimization was solved by using SNOPT a general nonlinearly-constrained optimization package
  • The optimization time depends on the duration of the animation
  • All of the simple animation took less than five minutes to sketch
  • For all examples the synthesis process took less than five minutes

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

broad jump
Broad jump
  • Only 3 keyframes at takeoff, peak and landing

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

running
Running
  • The angular momentum constraint creates a counter-body movement by the shoulders and arms to counteract the angular momentum generated by the legs.
  • Keyframing 7 DOFs

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

hopscotch
Hopscotch
  • Each hop requires 3 keyframes and has fewer than 7 DOFs

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

handspring
Handspring
  • There were no handstands within the DB so the user had to modify the result

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

high bar
High-bar
  • Two constraints stages: the bar and ground

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

karate kick
Karate kick
  • A second synthesis add a keyframe in the peak

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

twist jumps
Twist jumps

C. Karen Liu & Zoran Popovi´c "Synthesis of Complex Dynamic Character Motion from Simple Animations"

agenda116
Agenda

- Basic concepts

- Motion Synthesis/texture using motion capture

  • Physics/Biomechanics Motion Synthesis

- Cartoon Motion Retargeting

what is cartoon capture retargeting
What is Cartoon Capture & Retargeting
  • Cartoon Capture
    • Track the motion From 2D Animation
    • Represent the motion & save
  • Retargeting
    • Translate the motion representation to another output media

C.Bregler, L.Loeb, E.Chuang, H.Deshpande; "Turning to the Masters: Motion Capturing Cartoons”

slide118

Cartoon motion capture & retargeting scheme

Digitized video

Output video

Motion representation

Cartoon capture

retargeting

Key shapes

Output corresponding

key shapes

C.Bregler, L.Loeb, E.Chuang, H.Deshpande; "Turning to the Masters: Motion Capturing Cartoons”

modeling cartoon motion

Digitized video

Output video

Motion representation

Cartoon capture

retargeting

Key shapes

Output corresponding

key shapes

Modeling Cartoon motion

C.Bregler, L.Loeb, E.Chuang, H.Deshpande; "Turning to the Masters: Motion Capturing Cartoons”

modeling cartoon motion120
Modeling Cartoon motion
  • Two types of deformations
    • Affine deformation
    • Key shape deformation

C.Bregler, L.Loeb, E.Chuang, H.Deshpande; "Turning to the Masters: Motion Capturing Cartoons”

affine deformation

y

x

Affine Deformation
  • Affine parameters

C.Bregler, L.Loeb, E.Chuang, H.Deshpande; "Turning to the Masters: Motion Capturing Cartoons”

key shape deformation
Key-Shape Deformation
  • Sk are the key shapes

C.Bregler, L.Loeb, E.Chuang, H.Deshpande; "Turning to the Masters: Motion Capturing Cartoons”

modeling cartoon motion123
Modeling Cartoon motion
  • In total there are 6+K variables that represent the motion

C.Bregler, L.Loeb, E.Chuang, H.Deshpande; "Turning to the Masters: Motion Capturing Cartoons”

cartoon motion capture

Digitized video

Output video

Motion representation

Cartoon capture

retargeting

Key shapes

Output corresponding

key shapes

Cartoon motion capture

C.Bregler, L.Loeb, E.Chuang, H.Deshpande; "Turning to the Masters: Motion Capturing Cartoons”

cartoon motion capture125
Cartoon motion capture
  • contour capture: the input is a sequence of contours
  • video capture: the input is the video sequence

C.Bregler, L.Loeb, E.Chuang, H.Deshpande; "Turning to the Masters: Motion Capturing Cartoons”

contour capture
contour capture
  • Two step minimization:
    • Find Affine parameters
    • Find Key-Shape weights
  • Iterate

C.Bregler, L.Loeb, E.Chuang, H.Deshpande; "Turning to the Masters: Motion Capturing Cartoons”

retargeting

Digitized video

Output video

Motion representation

Cartoon capture

retargeting

Key shapes

Output corresponding

key shapes

Retargeting

C.Bregler, L.Loeb, E.Chuang, H.Deshpande; "Turning to the Masters: Motion Capturing Cartoons”

retargeting128
Retargeting
  • For each Input key-shape an Output key-shape is drawn.

C.Bregler, L.Loeb, E.Chuang, H.Deshpande; "Turning to the Masters: Motion Capturing Cartoons”

retargeting process
Retargeting Process

Key shapes Interpolation

Retarget Motion capture

Apply Affine transformation

From motion capture

Retargeted media

C.Bregler, L.Loeb, E.Chuang, H.Deshpande; "Turning to the Masters: Motion Capturing Cartoons”

examples
Examples

C.Bregler, L.Loeb, E.Chuang, H.Deshpande; "Turning to the Masters: Motion Capturing Cartoons”

additional constrains post processing
Additional constrains & post processing
  • Undesirable effects may still appear
  • Determine constraints that force the character go through certain position at certain time
  • Apply ad-hoc global transformation that fulfill these constraints

C.Bregler, L.Loeb, E.Chuang, H.Deshpande; "Turning to the Masters: Motion Capturing Cartoons”

performance
Performance
  • Quantative performance wasn’t mentioned
  • The more complex the motion of the character is, the more key-shapes are needed
  • Many of the animations contain jitter, but the overall exaggerated motion dominates

C.Bregler, L.Loeb, E.Chuang, H.Deshpande; "Turning to the Masters: Motion Capturing Cartoons”