Learning the Appearance of Faces: A Unifying Approach for the Analysis and Synthesis of Images.

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Learning the Appearance of Faces: A Unifying Approach for the Analysis and Synthesis of Images. Thomas Vetter. University of Freiburg. Germany. http://graphics.informatik.uni-freiburg.de. Computer Vision &amp; Computer Graphics.

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### Learning the Appearance of Faces:A Unifying Approach for the Analysis and Synthesis of Images.

Thomas Vetter

University of Freiburg

Germany

http://graphics.informatik.uni-freiburg.de

Computer Vision & Computer Graphics

Vision ( image) parameters

image Graphics ( parameters )

-1

G( image ) Parameters

| G(p) - I |2 = minParameters

Computer Graphics can help to solve Computer Vision!

Analysis by Synthesis

model parameter

Analysis

Image Model

Synthesis

Image

3D

World

Image Description

Synthesis of Faces

Database

Morphable

Face Model

Face

Analyzer

Modeler

Result

Input Image

Approach: Example based modeling of faces

2D Image 3D Face Models

= w1 * + w2 * + w3 * + w4 * +. . .

Cylindrical Coordinates

h

f

h

f

red(h,f)

green(h,f)

blue(h,f)

Morphing 3D Faces

1

__

2

3D Blend

3D Morph

1

__

=

+

2

Correspondence: A two step process!

2nd Example

Example

Reference

• Correspondence between
• two examples ( Optical Flow like algorithms).
• many examples ( Morphable Model )
Vector space of 3D faces.
• A Morphable Model can generate new faces.

a1 * + a2 * + a3 * + a4 * +. . .

=

b1 * + b2 * + b3 * + b4 * +. . .

Modelling in Face Space

Caricatur

Original

Average

Modelling the Appearance of Faces

A face is represented as a point in face space.

• Which directions code for specific attributes ?
Learning from Labeled Example Faces

Fitting a (linear) regression function

Facial Attributes

Subjective Attractiveness

Weight

Original

Transfer of Facial Expressions

-

= Smile

Novel Face:

+ Smile =

Originals:

3D Shape from Images

Face

Analyzer

Input Image

Matching a Morphable 3D-Face-Model

Optimization problem!

a1 * + a2 * + a3 * + a4 * +. .

= R

b1 * + b2 * + b3 * + b4 * +. .

Error Function
• Image difference
• Plausible parameters
• Minimize
Optimization Strategies
• Difference Decomposition