Pose Space Deformation - ETHZ · Pose Space Deformation Body Animation 1. Pose Space ... –No Poes...

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Pose Space DeformationA unified Approach to Shape Interpolation and Skeleton-Driven

Deformation

J.P. Lewis Matt Cordner Nickson Fong

Presented by Simone Croci

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Talk Outline• Character Animation

OverviewProblem Statement

• BackgroundSkeleton-Subspace DeformationShape Interpolation

• Pose Space DeformationDeformation ModelEvaluationRelated Works

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Character Animation

OverviewAnimation:

“To give a soul to a lifeless character”

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Character Animation

Problem StatementMain Components:

1. Character Model

2. Deformation model

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Character Animation

Deformation Model

Complexity:∀ vertex: 3 DoFs

=> Reduction of DoFsx

y

z

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Character Animation

Deformation Model

Mapping:Parameters => Deformation

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Character Animation

Deformation ModelJoint rotations of a skeleton

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Character Animation

Deformation ModelEmotional Axes

X=Sad/Happy Y=Relaxed/ Excited

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Character Animation

Deformation ModelReduction of DoFs

ReducedDeformation Freedom

MovementDeficiencies

Lack of realism

Difficulty tocontrol the deformations

PathologicalDefects

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Character Animation

Deformation ModelReduction of DoFs

ReducedDeformation Freedom

solves these problems

retaining few DoFs

MovementDeficiencies

Lack of realism

Difficulty tocontrol the deformations

PathologicalDefects

Pose Space Deformation

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Skeleton-Subspace Deformation

…also called skinning, enveloping

Deformation ModelSkeleton => Vertex Position

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Initial posey

x

v

v’’

v’1. Local

coordinatesv’,v’’

2. Vertex-joint weights: w`, w``

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New posey

x

v

v = w` T’(v`)+ w`` T’’(v``)

v’v’’

T from local toworld coordinates

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Pros• Simple• Smooth deformations• Low memory requirements• Real-time deformations

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Cons• Indirect control on deformations through

weights

• Deformation Subspace is limited=> Pathological Defects (complex Joint

configurations, “Collapsing Elbow”)

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Collapsing Elbow

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Frame 1 Frame 2

Initial pose

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Frame 1

New pose

Frame 2

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Cons• Indirect control on deformations through weights

PSD: direct sculpting

• Deformation Subspace is limited=> Pathological Defects (complex Joints, “Collapsing Elbow”)

PSD: interpolation of a vast range of deformations

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Shape Interpolation

∑=k

kkSwS

also called shape blending, multi-target morphing

AlgorithmInput: key shapes Sk

(same topology)

SuperpositionAnger Disgust Fear

Joy Sadness Surprise

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Shape Interpolation

neutral smirk

+ =>

half smirk

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Shape Interpolation

smirk

+ )=½(half smirkneutral

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Shape Interpolation

smirk

+ )=½(half smirkneutral

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Tony de Peltrie1985

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Shape Interpolation

Pros• Direct manipulation of desired expressions• Skin deformation for facial animation

(Used in the movies)

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Shape Interpolation

Cons• Not suited for skeleton-based deformations

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Shape Interpolation

Cons• Not suited for skeleton-based deformations• Storage expensive

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Shape Interpolation

Cons• Not suited for skeleton-based deformations• Storage expensive• Conflicting Key Shapes

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Shape Interpolation

Conflicting Key Shapes

Key Shapes are not independent

Superpositiona KSHappy+ b KSSurprise

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Shape Interpolation



Superpositiona KSHappy+ b KSSurprise + c KSFear

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Shape Interpolation



Superpositiona’ KSHappy+ b’ KSSurprise + c KSFear

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Shape Interpolation



Superpositiona’ KSHappy+ b’ KSSurprise + c KSFear

PSD: interpolation between key Shapes

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Shape Interpolation

Cons• Not suited for skeleton-based deformations• Storage expensive• Conflicting Key Shapes• Key Shape <=> Animation Parameter

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Shape Interpolation

Key Shape Anim. Parameter

If new expression is needed=> New Key shape & New Parameter

∑=k

kkSwS

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Shape Interpolation

Key Shape Anim. Parameter

If new expression is needed=> New Key shape & New Parameter

PSD: no additional parameter

freedom in the design of parameter space

∑=k

kkSwS

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Comparison TablePose Space Deformation

Low requirementsHigh requirementsMemory

YesNot alwaysSmoothness of Interpolation

NoYesDirect manipulation

Real-timeReal-timePerformance

Body (skeleton-influenced) Deformation

Facial Deformation

Field of application

Skeleton-subspace Deformation

Shape Interpolation

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Comparison Table

Real-time







Facial Deformation



Shape Interpolation

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Comparison Table

Low requirements

Real-time







Facial Deformation



Shape Interpolation

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Comparison Table

If needed

Low requirements

Real-time







Facial Deformation



Shape Interpolation

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Comparison Table

Yes

If needed

Low requirements

Real-time







Facial Deformation



Shape Interpolation

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Comparison Table

Facial and Body Deformations

Yes

If needed

Low requirements

Real-time







Facial Deformation



Shape Interpolation

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Character Animation

Deformation Model


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Character Animation

Deformation Model


Interpolat from deformation examples


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Character Animation

Deformation Model

Mapping:Parameters => DeformationPose Space => Displacements Δx


Interpolat from deformation examples

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Face Animation1. Pose Space

Ex: Emotion Space

2. Initial Face Model

3. Sculpting of exampleswith Pose Space Coordinates[Δx: between initial model and examples]

4. Interpolant

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Face Animation

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Body Animation1. Pose Space

Ex: Skeleton Parameters

2. Skeleton & Model

3. Sculpting of exampleswith Pose Space Coordinates[Δx: between SSD and examples]

4. Interpolant

SSD PSD

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Body Animation

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Interpolat from ExamplesInterpolant:

Pose Space => Displacements Δxfrom {(PSCoord1, Δx1), ..., (PSCoordN, ΔxN)}

Scattered Data Interpolation:Interpolation of a set of irregularly located data points

Approaches:- Shepard’s Method- Radial Basis Function Interpolation

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Shepard’s Method

are the given data points at

0,)( >−= − pw pkk xxx

∑

∑

=

== N

kk

N

kkk

w

dwd

1

1

)(

)()(ˆ

x

xx

Ndd ,,1 K Nxx ,,1 K

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Weight function

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Flat zones

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Average at far points

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Shepard’s Method

Properties:• Singular at data points ( )• Infinitely differentiable except at data points• Partial derivatives are zero at data points

=> flat zones around data points• Far from data points converges to the

average value

kx

N

d

w

dwd

N

kk

N

kk

N

kkk ∑

∑

∑=

=

= =∞

∞=∞ 1

1

1

)(

)()(ˆ

0with )( >−= − pw pkk xxx

kx

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Radial Basis Functions…also called Hardy’s multiquadratics

from at solving a system of linear equations:

( )∑=

=N

kkkwd

1-)(ˆ xxx ψ

Nww ,,1 K Ndd ,,1 K Nxx ,,1 K

( ) ( )

( ) ( ) ⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛

⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛=

⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛

NNNN

Nk

N w

w

d

dM

L

MOM

L

M1

1

111

--

--

xxxx

xxxx

ψψ

ψψ

wd Ψ=

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Choice of Radial Basis Function

( ) rr =ψ

kr xx -=

( ) 0,)( 22 >+= − ασψ αrr

( ) )ln(2 rrr =ψ

( ) 10,)( 22 <<+= βσψ βrr

( ) 3rr =ψ

localized

Thin-plate spline function

linear

cubic

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Gaussian Radial Basis

=>

( ) ⎟⎟⎠

⎞⎜⎜⎝

⎛ −= 2

2

2exp

σψ rr

kr xx -=

∑= ⎟

⎟

⎠

⎞

⎜⎜

⎝

⎛ −=

N

k

kkwd

12

2

2-

exp)(ˆσxx

x

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Gaussian Basis Function

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Properties:- smooth- localized:

=> width of falloff is adjustable- relatively fast to compute (table)

( ) ∞→= rr for0ψσ

( ) ⎟⎟⎠

⎞⎜⎜⎝

⎛ −= 2

2

2exp

σψ rr

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Pose Space Deformation[Summary]

∑=

=N

kkwd

1)()(ˆ xx ψ

Deformation model:Interpolant:

Pose Space => Displacements Δx

from {(PSCoord1, Δx1), ..., (PSCoordN, ΔxN)}

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Workflow

l

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PerformanceFor N poses:

• Preprocessing phase:- NxN Matrix must be inverted- Matrix-vector multiplication

(∀ component of ∀ displaced vertex)

• Animation phase:- Interpolated table lookup

wd Ψ=

dw 1−Ψ=Ψ

( )∑=

=N

kkkwd

1-)(ˆ xxx ψ

( ) ( )

( ) ( )⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛=Ψ

NNN

Nk

xxxx

xxxx

--

--

1

11

ψψ

ψψ

L

MOM

L

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Memory Requirements

For N poses:Every vertex stores Nx3 weights

(N weights ∀ component of a vertex)N pose space coordinates of ex.

( )∑=

=N

kkkwd

1-)(ˆ xxx ψ

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Memory Requirements



( )∑=

=N

kkkwd

1-)(ˆ xxx ψ

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Memory Requirements



( )∑=

=N

kkkwd

1-)(ˆ xxx ψ

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Pros & ConsPros:• Wide range of deformations• Arbitrary Pose Space Axes• Real-time synthesis • Relatively simple to implement• Control on interpolation ( )• Direct manipulation

(Iterative layered refinement)

σ

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Pros & Cons

Cons:• Accuracy is reliant on the modeler/animator• is manually tuned• Performance• Memory requirements

σ

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Critiques

• Least Square Problem ?with regular

• If N poses then 3 NxN matrices must be inverted for each control vertex?

Only one NxN matrix must be inverted• Close coordinates in PS results in a

numerically unstable matrix (regularization, TSVD)

dΨw 1−=( ) dΨΨΨw TT 1−

=Ψ

Ψ

Ψ

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Related Papers

Shape from ExamplesSloan, Rose, Cohen (I3D conference 2001)

EigenSkinKry, James, Pai (SIGGRAPH 2002)

Skinning Mesh AnimationJames, Twigg (SIGGRAPH 2005)

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Shape by ExamplesSloan, Rose, Cohen

• Paradigm: Design by Examples• Interpolation in Lagrangian form• Radial Basis Functions (B-Splines) and Polynomials• Interpolation & Extrapolation• Reparameterization• Applied also to Textures• Preprocessing phase:

– one linear system per example• Animation phase: twice faster

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Shape by ExamplesSloan, Rose, Cohen

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EigenSkinKry, James, Pai

• Paradigm: Design by Examples• Articulated characters• Deformation field for each Joint support

– EigenDeformations (Deformation basis)• Mapping: Joint => Eigendeformation coordinates

– 1-D interpolation with Radial Basis Functions– No non-linear joint-joint coupling effects

• Graphical Hardware Optimization– Reduction of Memory Requirements– Real-time rendering

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EigenSkinKry, James, Pai

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Skinning Mesh AnimationJames, Twigg

• Approximation of sequence of input meshes– No Pose Space

• Skinning algorithm:– Proxy bone Transformations (+ weights)

automatically approximated– Displacement field in rest pose (TSVD)

• Real-time rendering (Graphical HW support)

... more to come

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