### Faces and Image-Based Lighting

Digital Visual Effectsg
*Yung-Yu Chuang*

*with slides by Richard Szeliski, Steve Seitz, Alex Efros, Li-Yi Wei and Paul Debevec*

**Outline**

• Image-based lighting

• 3D acquisition for faces

• Statistical methods (with application to face super-resolution)p )

• 3D Face models from single images

• Image based faces

• Image-based faces

• Relighting for faces

**Image-based lighting** **Image based lighting**

**Rendering**

• Rendering is a function of geometry, reflectance lighting and viewing reflectance, lighting and viewing.

• To synthesize CGI into real scene, we have to t h th b f f t

match the above four factors.

*• Viewing can be obtained from calibration or *
*structure from motion.*

*• Geometry can be captured using 3D *y p g
*photography or made by hands.*

• How to capture lighting and reflectance?

• How to capture lighting and reflectance?

**Reflectance**

• The Bidirectional Reflection Distribution Function

Given an incoming ray and outgoing ray – Given an incoming ray and outgoing ray

what proportion of the incoming light is reflected along outgoing ray?

surface normal surface normal

Answer given by the BRDF:

**Rendering equation**

)
ω
,
p
( _{i}
*L**i*

ωi

)
ω
,
p
( _{i}
*L**i*

p ωo

) ω p,

( _{o}

*L**o*

*5D light field*
)

(

*L* *L* ( )

*5D light field*

) ω p,

( _{o}

*L**o* *L** _{e}*(p,ω

_{o})

i i i

i

o,ω ) (p,ω )cosθ ω

ω p,

2 ( *L*_{i}*d*

###

_{2}

###

(p,_{o},

_{i})

*(p,*

_{i}_{i})

_{i}

_{i}

###

*s*

^{}

**Complex illumination**

) ω p,

( _{o}

*L**o* *L** _{e}*(p,ω

_{o})

###

*s*

^{2}

^{f}^{(}

^{p,}

^{ω}

^{o}

^{,}

^{ω}

^{i}

^{)}

^{L}

^{i}^{(}

^{p,}

^{ω}

^{i}

^{)}

^{cos}

^{θ}

^{i}

^{d}^{ω}

^{i}

###

###

) ω p,

( _{o}

*B* _{2} *f*(p,ω_{o},ω_{i})*L** _{d}*(p,ω

_{i})cosθ

_{i}

*d*ω

_{i}

###

*s*

###

)
ω
( _{o}

*B**p* _{2} _{,}_{ω} (ω_{i}) (ω_{i})cosθ_{i} ω_{i}

o *L* *d*

*f* _{d}

*s* *p*

###

**p****q**

**Point lights**

Classically, rendering is performed assuming point light sources

light sources

directional source

**Natural illumination**

People perceive materials more easily under natural illumination than simplified illumination natural illumination than simplified illumination.

I t R D d T d Ad l

Images courtesy Ron Dror and Ted Adelson

**Natural illumination**

Rendering with natural illumination is more expensive compared to using simplified expensive compared to using simplified illumination

directional source natural illumination

**Environment maps**

Miller and Hoffman 1984 Miller and Hoffman, 1984

**HDR lighting**

**Examples of complex environment light** **Examples of complex environment light**

**Complex illumination**

) ω p,

( _{o}

*L**o* *L** _{e}*(p,ω

_{o})

###

*s*

^{2}

^{f}^{(}

^{p,}

^{ω}

^{o}

^{,}

^{ω}

^{i}

^{)}

^{L}

^{i}^{(}

^{p,}

^{ω}

^{i}

^{)}

^{cos}

^{θ}

^{i}

^{d}^{ω}

^{i}

###

###

) ω p,

( _{o}

*B* _{2} *f*(p,ω_{o},ω_{i})*L** _{d}*(p,ω

_{i})cosθ

_{i}

*d*ω

_{i}

###

*s*

###

)
ω
( _{o}

*B**p* _{2} _{,}_{ω} (ω_{i}) (ω_{i})cosθ_{i} ω_{i}

o *L* *d*

*f* _{d}

*s* *p*

###

reflectance lighting B th h i l f ti Both are spherical functions

**Function approximation**

• G(x): the function to approximate

• B_{1}(x), B_{2}(x), … B_{n}(x): basis functions

• We want

### ) ( )

### ( *x* *c* *B* *x*

*G* ^{(} ^{)} ^{}

^{n}

_{i}

_{i}^{(} ^{)}

1

*x* *B* *c* *x*

*G*

_{i}*i*

###

*i*

• Storing a finite number of coefficients c_{i} gives
an approximation of G(x)

**Function approximation**

• How to find coefficients c_{i}?
Mi i i

– Minimize an error measure

• What error measure?

– L_{2} error

###

^{[}

^{(}

^{)}

^{(}

^{)}

^{]}2

^{2}

2 ^{}

###

^{}

###

*I* *i*

*i*
*i*

*L* *G* *x* *cB* *x*

*E*

### • Coefficients

###

^{G}

^{x}

^{B}

^{x}

^{dx}*B*
*G*

*c* ^{} ^{}

###

( ) ( )*X*

*i*
*i*

*i* *G* *B* *G* *x* *B* *x* *dx*

*c* ( ) ( )

**Function approximation**

• Basis Functions are pieces of signal that can be used to produce approximations to a function

produce approximations to a function

### ^{} ^{} ^{c}

^{c}

^{1}

### ^{} ^{c}

^{c}

^{2}

###

###

###

2

### *c*

### ^{} ^{c}

^{c}

^{3}

**Function approximation**

• We can then use these coefficients to reconstruct an approximation to the original signal

approximation to the original signal

1

###

*c*

### *c*

2
###

2

###

*c*

_{3}

^{}

*c*

**Function approximation**

• We can then use these coefficients to reconstruct an approximation to the original signal

approximation to the original signal

### ^{}

###

^{N}^{c}

^{c}

^{i}^{B}

^{B}

^{i}^{ } ^{x}

^{x}

###

*i*

*i*

*i*1

**Orthogonal basis functions**

• Orthogonal Basis Functions

Th f ili f f ti ith i l – These are families of functions with special

properties

###

###

###

###

###

### ^{B}

^{B}

^{i}^{x} ^{B}

^{x}

^{B}

^{j}^{x} ^{dx} _{} ^{1} _{0} _{i} ^{i} _{} _{j} ^{j}

^{x}

^{dx}

_{i}

^{i}

_{j}

^{j}

### _{0} _{i} _{j}

_{i}

_{j}

– Intuitively, it’s like functions don’t overlap each other’s footprint

A bit lik th F i t f b k

• A bit like the way a Fourier transform breaks a functions into component sine waves

**Integral of product**

###

###

### *F* *x* *G* *x* *dx* *I*

### ^{x} ^{} ^{f} ^{B} ^{x}

^{x}

^{f}

^{B}

^{x}

*F* ( ) ^{G} ^{x} ^{} ^{g} ^{B} ^{(} ^{x} ^{)}

^{G}

^{x}

^{g}

^{B}

^{x}

###

### ^{} ^{} ^{} ^{}

### ^{}

*i*

*i*

*i*

*B* *x*

*f* *x*

*F* ( ) ^{}

*j*

*j*

*j*

*B* *x*

*g* *x*

*G* ( )

###

###

###

###

###

*i* *j*

*j*
*j*
*i*

*i*

*B* *x* *g* *B* *x* *dx*

*f* *dx*

*x* *G* *x*

*F* ( ) ( )

###

### ^{} ^{} ^{}

###

###

###

*i*
*i*
*j*

*i*
*j*

*i*

*g* *B* *x* *B* *x* *dx* *f* *g* *dx* *F* *G*

*f* ( ) ( ) ˆ ˆ

###

*i*

*j*

*i*

) ω (

*B*^{p}^{(}^{ω}^{o}^{)}^{}

###

*f*(ω )

*L*(ω )cosθ

*d*ω

*B*

_{2}

_{,}

_{ω}(ω

_{i}) (ω

_{i})cosθ

_{i}ω

_{i}

o *L* *d*

*f* _{d}

*s* *p*

###

**Basis functions**

• Transform data to a space in which we can capture the essence of the data better capture the essence of the data better

• Spherical harmonics, similar to Fourier

t f i h i l d i i d i PRT transform in spherical domain, is used in PRT.

**Real spherical harmonics**

• A system of signed, orthogonal functions over the sphere

the sphere

• Represented in spherical coordinates by the f ti

function

###

### 2 *K*

^{m}*P*

^{m}### 0

###

###

###

###

###

###

###

###

^{}

### 0

### 0 ,

### cos sin

### 2

### , cos cos

### 2

### , *m*

*m* *P*

*m* *K*

*P* *m* *K*

*y*

_{l}

^{m}

_{l}

^{m}*m*
*l*
*m*

*l*
*m*

*l*

###

###

###

###

###

###

###

###

###

### 0 0 ,

### cos

### , cos sin

### 2 ,

0

0

*m*

*m* *P*

*K*

*P* *m* *K*

*y*

*l*
*l*

*l*
*l*

*l*

###

###

###

###

###

*where l is the band and m is the index within the band*

###

**Real spherical harmonics** **Reading SH diagrams**

**This** **di** **i** **direction**

**–** **+** **+**

**Not this** **direction**

**Reading SH diagrams**

**This** **di** **i** **direction**

**–** **+** **+**

**Not this** **direction**

**The SH functions**

###

0

*y*

0_{0}

*y*

###

1

*y*

1 ### *y*

_{1}

^{1}

1

*y*

2 *y*

_{2}

^{2}

0

*y*

2
1
2

*y*

2
2

*y*

**The SH functions** **Spherical harmonics**

**Spherical harmonics**

**0**

^{m} *Y* ( )

^{m}**0**

*Y* _{lm} ( , )

_{lm}

### 1

**1**

**l**

**l**

**1**

_{y} *z* *x*

_{y}

**22**

*xy* *yz* 3 *z*

^{2}

### 1 *zx* *x*

^{2}

### *y*

^{2}

**-1**

**-2** **0** **1** **2**

**SH projection**

**• First we define a strict order for SH functions**

### ^{l} ^{m}

^{l}

^{m}

*l*

*i* 1

**• Project a spherical function into a vector of**

**• Project a spherical function into a vector of**
**SH coefficients**

###

###

###

_{i}*i*

*f* *s* *y* *s* *ds*

*c*

*S*

*i*
*i*

**SH reconstruction**

• To reconstruct the approximation to a function
*N*2

### ^{}

### ~

^{N}*i*

*i*

*y* *s*

*c* *s*

*f*

0
*i*

• We truncate the infinite series of SH functions
**to give a low frequency approximation**g **q** **y pp**

**Examples of reconstruction**

**An example**

• Take a function comprised of two area light sources

sources

– SH project them into 4 bands = 16 coefficients

3290679 0930 0908
1*.* *,*

238 0 0 425

0642 0001 0317 0837 0940 0 0417 0 0278 0679 0930 0908 0

*,*
*.*
*,*
*.*
*,*
*.*
*,*

*.* *,* *,* *.* *,* *,* *.* *,*

*.* *,* *.* *,* *.* *,*

*.*

0*.*425*,*0*,*0*.*238

**Low frequency light source**

• We reconstruct the signal

U i l th ffi i t t fi d l f

– Using only these coefficients to find a low frequency approximation to the original light source

**SH lighting for diffuse objects**

*• An Efficient Representation for Irradiance *
*Environment Maps Ravi Ramamoorthi and Pat *
*Environment Maps, Ravi Ramamoorthi and Pat *
Hanrahan, SIGGRAPH 2001

A ti

• Assumptions

– Diffuse surfaces – Distant illumination

– No shadowing, interreflection

)
(*p,ω*_{o}

*B* _{2} *f*(p,ω_{o},ω_{i})*L** _{d}*(p,ω

_{i})cosθ

_{i}

*d*ω

_{i}

###

*s*

*s*

)
n
(
)
*( Ep*

###

*n)*
*B(p,*

irradiance is a function of surface normal

**Diffuse reflection**

*B* *E* *B* *E*

di i fl

radiosity (image intensity)

reflectance (albedo/texture)

irradiance (incoming light)

### = ×

k li h quake light map

**Irradiance environment maps**

L *n*

Illumination Environment Map Irradiance Environment Map

###

^{p}

^{ } ^{}

^{p}

### ^{}

### *L* *n* *d* *n*

*E )* (

**Spherical harmonic expansion**

Expand lighting (L), irradiance (E) in basis functions

*l*

### ( , )

^{l}

_{lm lm}### ( , ) *L*

^{}

^{}

*L Y*

0
*l* *m**l*

###

*l*

0

### ( , )

_{lm lm}### ( , )

*l* *l*

*E* *E Y*

0
*l* *m**l*

**= .67** **+ .36** **+ …**

**Analytic irradiance formula**

Lambertian surface

###

acts like low-pass

filter 2 / 3

*E* _{lm} *A L* _{l lm} ^{A}

_{lm}

_{l lm}

^{A}

^{l}^{}

^{/ 4}

*E* *A L*

_{0}

^{}

^{/ 4}

0 1 2

*l*

cosine term

21

2 2

( 1) !

2 ( 2)( 1) 2 !

*l*

*l* *l* *l*

*A* *l* *l even*

*l* *l*

^{} ^{} ^{} ^{}

2 ( )( ) 2 !

**9 parameter approximation**

i Order 0

Exact image Order 0

1 term

*m*

**RMS error = 25 %** ^{0}*Y** _{lm}*( , )

*l*
*m*

**1**
**2**

*y* *z* *x*

**-1**

**-2** **0** **1** **2**

**2** _{xy}*yz* 3*z*^{2}1 *zx* *x*^{2}*y*^{2}

**9 Parameter Approximation**

i Order 1

Exact image Order 1

4 terms

*m*

**RMS Error = 8%** ^{0}*Y** _{lm}*( , )

*l*
*m*

**1**
**2**

*y* *z* *x*

**-1**

**-2** **0** **1** **2**

**2** _{xy}*yz* 3*z*^{2}1 *zx* *x*^{2}*y*^{2}

**9 Parameter Approximation**

i Order 2

Exact image Order 2

9 terms

*m*

**RMS Error = 1%** ^{0}*Y** _{lm}*( , )

*l*
*m*

For any illumination, average error < 3% [Basri Jacobs 01]

**1**
**2**

*y* *z* *x*

error < 3% [Basri Jacobs 01]

**-1**

**-2** **0** **1** **2**

**2** _{xy}*yz* 3*z*^{2}1 *zx* *x*^{2}*y*^{2}

**Comparison**

Incident Irradiance map Irradiance map illumination

300x300

p Texture: 256x256

Hemispherical

p Texture: 256x256 Spherical Harmonic Integration 2Hrs Coefficients 1sec Time 300 300 256 256 Time 9 256 256

**Complex geometry**

Assume no shadowing: Simply use surface normal Assume no shadowing: Simply use surface normal

*y*

**Natural illumination**

For diffuse objects, rendering with natural illumination can be done quickly

illumination can be done quickly

directional source natural illumination

**Video**

**Acquiring the Light Probe**

**HDRI Sky Probe**

**Clipped Sky + Sun Source** **Lit by sun only** **y** **y**

**Lit by sky only** **y** **y** **y** **Lit by sun and sky** **y** **y**

**Illuminating a Small Scene**

**Real Scene Example**

• Goal: place synthetic objects on tableGoal: place synthetic objects on table

**Light Probe / Calibration Grid** **g**

**Modeling the Scene**

light-based model light-based model

real scene

**The Light-Based Room Model**

**The Light-Based Room Model**

**Rendering into the Scene**

• Background PlateBackground Plate

**Rendering into the scene**

• Objects and Local Scene matched to SceneObjects and Local Scene matched to Scene

**Differential rendering**

• Local scene w/o objects, illuminated by modelLocal scene w/o objects, illuminated by model

**Differential rendering**

### =

### - =

**Differential rendering**

### + +

**Differential Rendering**

• Final ResultFinal Result

**Environment map from single image?** **Eye as light probe! (Nayar et al)**

**Results** **Application in “Superman returns”**

**Capturing reflectance** **Application in “The Matrix Reloaded”**

**3D acquisition for faces** **3D acquisition for faces**

**Cyberware scanners**

face & head scanner whole body scannery

**Making facial expressions from photos**

• Similar to Façade, use a generic face model and view dependent texture mapping

and view-dependent texture mapping

• Procedure

1. Take multiple photographs of a person 2. Establish corresponding feature points 3. Recover 3D points and camera parameters 4. Deform the generic face model to fit points 5. Extract textures from photos

**Reconstruct a 3D model**

input photographs

generic 3D pose more deformed

generic 3D face model

p

estimation features model

**Mesh deformation**

– Compute displacement of feature points Apply scattered data interpolation – Apply scattered data interpolation

generic model displacement deformed model

**Texture extraction**

• The color at each point is a weighted combination of the colors in the photos combination of the colors in the photos

• Texture can be:

– view-independent – view-dependent

• Considerations for weighting

– occlusion – smoothness

– positional certaintyp y – view similarity

**Texture extraction** **Texture extraction**

**Texture extraction**

### view-independent view-dependent

**Model reconstruction**

Use images to adapt a generic face model Use images to adapt a generic face model.

**Creating new expressions**

• In addition to global blending we can use:

R i l bl di – Regional blending – Painterly interface

**Creating new expressions**

New expressions are created with 3D morphing:

**+** **=**

**+**

**/2** **/2**

Applying a global blend

**Creating new expressions**

### +

x

### +

x### =

Applying a region-based blend

**Creating new expressions**

**+** **+** **+**

**+** **+** **+**

**=**

Using a painterly interface

**Drunken smile** **Animating between expressions**

Morphing over time creates animation:

**“neutral”** **“joy”**

**Video** **Spacetime faces**

**Spacetime faces**

**black & white cameras**
**color cameras**

**video projectors**

**time**

**time**

Face surface Face surface

**time**

**stereo**

**time**

**stereo** **active stereo**

**time**

**spacetime stereo**

**stereo** **active stereo**

**Spacetime Stereo**

**time**

surface motion surface motion

time=1

**Spacetime Stereo**

**time**

surface motion surface motion

time=2

**Spacetime Stereo**

**time**

surface motion surface motion

time=3

**Spacetime Stereo**

**time**

surface motion surface motion

time=4

**Spacetime Stereo**

**time**

surface motion surface motion

time=5

**Spacetime Stereo**

**time**

surface motion surface motion

**Better **

**• spatial resolution**

* • temporal stableness*
time

**• temporal stableness**

**Spacetime stereo matching** **Video**

**Fitting** **FaceIK**

**Animation** **3D face applications: The one**

**3D face applications: Gladiator**

### extra 3M extra 3M

**Statistical methods** **Statistical methods**

**Statistical methods**

### para observed

### f(z)+

### z y

### para- meters

### observed signal )

### | ( max

### * *P* *z* *y*

*z* max *P* ( *z* | *y* )

^{Example: }super-resolution

*z*

*z*

### ) ( )

### |

### max *P* ( *y* *z* *P* *z*

###

super-resolution de-noising

de-blocking

### ) max (

*y* *P*

###

*z*de-blocking

Inpainting

### ) ( )

### | (

### min *L* *y* *z* *L* *z*

*z*

###

###

^{…}

**Statistical methods**

### para observed

### f(z)+

### z y

### para- meters

### observed signal )

### ( )

### | ( min

### * *L* *y* *z* *L* *z*

*z* min *L* ( *y* | *z* ) *L* ( *z* )

*z*

*z*

###

### )

2*(z* *f*

### data *y * *a-priori*

###

2### evidence knowledge

**Statistical methods**

*There are approximately 10*^{240}*possible 10**10 *
*There are approximately 10* *possible 10**10 *
*gray-level images. Even human being has not *
*seen them all yet. There must be a strong *
*seen them all yet. There must be a strong *
*statistical bias.*

*Takeo Kanade*
*Takeo Kanade*

Approximately 8X10^{11 }blocks per day per person.

**Generic priors**

### “S th i d i ”

### “Smooth images are good images.”

###

###

*x*

*x* *V* *z*

*L* ( ) ( ( ))

*x*

) 2

(*d * *d*

### Gaussian MRF

(*d*)

*d*

### Gaussian MRF

*T*
*d*

*d*

^{2}

*T*
*d*

*T*
*d*
*T*
*d*
*T*
*T*

*d* *d*

) (

) 2

( _{2}

### Huber MRF

**Generic priors** **Example-based priors**

### “E i ti i d i ”

### “Existing images are good images.”

### six 200200 Images Images 2,000,000 pairs

### pairs

**Example-based priors**

### L(z)

**Example-based priors**

high-resolution

low-resolution

**Model-based priors**

### “Face images are good images when Face images are good images when working on face images …”

### Parametric

### model Z=WX+ L(X)

### model

### ) ( )

### | ( min

### * *L* *y* *z* *L* *z*

*z* *(y* | ) ( )

*z*

### *X* * min *L* ( *y* | *WX* ) *L* ( *X* )

###

###

###

###

###

###

###

###

### *

### *

### ) ( )

### | ( min *WX* *z*

*X* *L* *WX*

*y* *L*

*X*

*x*

###

**PCA**

• Principal Components Analysis (PCA):

approximating a high dimensional data set approximating a high-dimensional data set with a lower-dimensional subspace

**

**

** **

** **

** ****

** **

**

** First principal componentFirst principal component Second principal component

Second principal component

Original axes Original axes

**

** ** **

**

******** **

**

****

** **

Data points Data points

**PCA on faces: “eigenfaces”**

Average

Average First principal componentFirst principal component Average

Average face face

Other Other components components

For all except average, For all except average,o a e cept a e age,o a e cept a e age,

“gray” = 0,

“gray” = 0,

“white” > 0,

“white” > 0,

“black” < 0

“black” < 0black < 0black < 0

**Model-based priors**

### “Face images are good images when Face images are good images when working on face images …”

### Parametric

### model Z=WX+ L(X)

### model

### ) ( )

### | ( min

### * *L* *y* *z* *L* *z*

*z* *(y* | ) ( )

*z*

### *X* * min *L* ( *y* | *WX* ) *L* ( *X* )

###

###

###

###

###

###

###

###

### *

### *

### ) ( )

### | ( min *WX* *z*

*X* *L* *WX*

*y* *L*

*X*

*x*

###

**Super-resolution**

(a) (b) (c) (d) (e) (f)

(a) Input low 24×32 (b) Our results (c) Cubic B-Spline (a) Input low 24×32 (b) Our results (c) Cubic B Spline (d) Freeman et al. (e) Baker et al. (f) Original high 96×128

**Face models from single images**

**Face models from single images**

**Morphable model of 3D faces**

• Start with a catalogue of 200 aligned 3D Cyberware scans

Cyberware scans

*• Build a model of average shape and texture *

*• Build a model of average shape and texture, *
*and principal variations using PCA*

**Morphable model**

shape examplars texture examplars

**Morphable model of 3D faces**

• Adding some variations

**Reconstruction from single image**

Rendering must be similar to the input if we guess right

g g

**Reconstruction from single image**

prior

shape and texture priors are learnt from database ρ is the set of parameters for shading including camera pose, lighting and so onp , g g

**Modifying a single image**

**Animating from a single image** **Video**

**Exchanging faces in images** **Exchange faces in images**

**Exchange faces in images** **Exchange faces in images**

**Exchange faces in images** **Morphable model for human body**

**Image-based faces** **(lip sync.)**

**Video rewrite (analysis)**

**Video rewrite (synthesis)** **Results**

• Video database

2 i t f JFK – 2 minutes of JFK

• Only half usable

• Head rotation

• Head rotation

training video R d li Read my lips.

I never met Forest Gump.

**Morphable speech model** **Preprocessing**

**Prototypes (PCA+k-mean clustering)**

W fi d I d C f h t t i

We find I_{i} and C_{i }for each prototype image.

**Morphable model**

### analysis

**I** ^{α β}

^{α β}

### analysis synthesis

**Morphable model**

### analysis synthesis

**Synthesis**

**Results** **Results**

**Relighting faces** **Relighting faces**

**Light is additive**

lamp #1 lamp #2

**Light stage 1.0** **Light stage 1.0**

64x32 lighting directions

**Input images** **Reflectance function**

occlusion flare

**Relighting** **Results**

**Changing viewpoints** **Results**

**3D face applications: Spiderman 2** **Spiderman 2**

real synthetic

real synthetic

**Spiderman 2**

**video**
**video**

**Light stage 3**

**Light stage 6** **Application: The Matrix Reloaded**

**Application: The Matrix Reloaded** **References**

• Paul Debevec, Rendering Synthetic Objects into Real Scenes:

Bridging Traditional and Image-based Graphics with Global Illumination and High Dynamic Range Photography

Illumination and High Dynamic Range Photography, SIGGRAPH 1998.

• F. Pighin, J. Hecker, D. Lischinski, D. H. Salesin, and R.

Szeliski Synthesizing realistic facial expressions from Szeliski. Synthesizing realistic facial expressions from photographs. SIGGRAPH 1998, pp75-84.

• Li Zhang, Noah Snavely, Brian Curless, Steven M. Seitz, S ti F High R l ti C t f M d li g d Spacetime Faces: High Resolution Capture for Modeling and Animation, SIGGRAPH 2004.

• Blanz, V. and Vetter, T., A Morphable Model for the S th i f 3D F SIGGRAPH 1999 187 194 Synthesis of 3D Faces, SIGGRAPH 1999, pp187-194.

• Paul Debevec, Tim Hawkins, Chris Tchou, Haarm-Pieter Duiker, Westley Sarokin, Mark Sagar, Acquiring the R fl t Fi ld f H F SIGGRAPH 2000 Reflectance Field of a Human Face, SIGGRAPH 2000.

• Christoph Bregler, Malcolm Slaney, Michele Covell, Video Rewrite: Driving Visual Speeach with Audio, SIGGRAPH 1997.

• Tony Ezzat, Gadi Geiger, Tomaso Poggio, Trainable Videorealistic Speech Animation, SIGGRAPH 2002.