AN EFFICIENT TEXTURE GENERATION TECHNIQUE FOR

HUMAN HEAD CLONING AND MORPHING

Yu Zhang

Department of Computer Science, School of Computing

National University of Singapore, Singapore 117543

Keywords:

Head modeling, texture mapping, real-time rendering, morphing, model adaptation.

Abstract:

This paper presents a technique to efﬁciently generate a parameterized full-head texture from a single face

image of the scanned data for modeling photorealistic 3D heads. We automatically register face scans in a

database by deforming a generic head mesh to ﬁt the speciﬁc person’s face geometry with a volume morphing

approach. After the face texture is transferred onto it, the 3D generic mesh is parameterized over a 2D texture

domain to establish a correspondence between all the scanned textures. After having performed a vertex-to-

image binding for all vertices of the head mesh, we automatically generate a full-head texture using color

interpolation for unbound regions and weighted average splines for visual boundary removal. We also use

a deformation method to extract ear textures from the input image for texturing individual ears. With the

exception of the initial feature point selection, our method is fully automated. We show photorealistic and

real-time head rendering and morphing with the resulting texture.

1 INTRODUCTION

Among the tasks of reproducing the complexity of

the face in the computer, rendering is crucial for gen-

erating visually convincing appearance of a particu-

lar person’s face. Basic rendering algorithms include

polygonal shading (ﬂat shading, Gouraud shading and

Phong shading), ray tracing and radiosity. However,

facial skin has reﬂectance properties that are not mod-

eled well by these widely-used shading models: the

produced facial image appears plastic and cartoon-

like with a too-smooth surface.

For sheer photorealism, texture mapping is a pop-

ular technique to get a better rendered image. With

a signiﬁcant increase in the quality and availability

of 3D capture methods, a common approach towards

creating face models of real humans uses laser range

scanners to accurately acquire both the face geometry

and texture. One limitation of the scanner technol-

ogy, however, is that the complete head geometry can

not be easily captured as the hair in dark color absorbs

all of the laser radiation. The top and back of the head

are generally not digitized unless the hair is artiﬁcially

colored white or the subject wears a light-color cap,

but that destroys the texture. In most cases, only the

frontal face can be properly textured. There is no au-

tomatic mechanism provided to generate a full-head

texture from the acquired single frontal-face image

for realistic rendering towards a “cloned” head.

In this paper, we present a technique to efﬁciently

generate a parameterized full-head texture for model-

ing heads with a high degree of realism. We start with

a generic head model with a known topology. It is

deformed to ﬁt the face scan of the particular human

subject using a volume morphing approach. The fa-

cial texture associated with the scanned geometry is

then transferred to the original undeformed generic

mesh. We automatically construct a parameteriza-

tion of the 3D head mesh over a 2D texture domain,

which gives immediate correspondence between all

the scanned textures via a single, prototype layout.

After having performed a vertex-to-image binding for

vertices of the head mesh, we generate a cylindrical

full-head texture from the remaining parameterized

texture of the face area. We also address the creation

of individual textures for ears. Apart from an initial

feature point selection for the texturing, our method

works automatically without any user interaction.

Our main contribution is a technique that uses a

frontal-face image of the scanned data to generate

a full-head texture for photorealistic rendering and

morphing with minimal manual intervention. This

includes the new algorithms to automatically para-

267

Zhang Y. (2006).

AN EFFICIENT TEXTURE GENERATION TECHNIQUE FOR HUMAN HEAD CLONING AND MORPHING.

In Proceedings of the First International Conference on Computer Graphics Theory and Applications, pages 267-275

DOI: 10.5220/0001352202670275

 SciTePress

meterize textures of a set of unregistered face scans

to establish the mapping correspondence, to robustly

produce individual full-head skin texture, and to efﬁ-

ciently create ear textures from a single input image.

This paper is organized as follows. Section 2 re-

views the previous work. Section 3 presents the face

data we use. Section 4 describes the model adapta-

tion process. The approaches to mesh parameteriza-

tion, full-head texture synthesis, and creation of ear

textures are elaborated in Section 5. We show exper-

imental results in Section 6. Section 7 concludes by

discussing future work.

2 PREVIOUS WORK

Beginning with Parke’s pioneering work (Parke,

1972), desire for improved realism has driven re-

searchers to extend geometric models (Parke, 1982;

Thalmann et al., 1989) with physically-based models

which attempt to model the inﬂuence of muscle con-

traction onto the skin surface by approximating the

biomechanical properties of skin (Kahler et al., 2002;

Lee et al., 1995; Platt and Badler, 1981; Terzopoulus

and Waters, 1990; Waters, 1987). Physically-based

models with layered anatomical structure were com-

bined with non-linear ﬁnite element methods (Koch

et al., 1996) in systems that could be used for plan-

ning facial surgeries. Free-form deformations have

been employed in (Kalra et al., 1992) to manipu-

late facial expressions. In parallel, Williams presents

a compelling argument (Williams, 1990) in favor of

performance-driven facial animation, which antici-

pated techniques for tracking head motions and facial

expressions in video (Essa and Basu, 1996; Pighin

et al., 1999). In performance-driven approaches, col-

ored markers painted on the face are extensively used

to aid in tracking facial motion (Guenter et al., 1998).

A more expensive alternative could use a 3D scanning

technique (Zhang et al., 2004a), if the performance

can be re-recorded with such a system.

A variational approach is presented in (DeCarlo

et al., 1998) to create a range of static face models

with realistic proportions. They use anthropometric

measurements, which constrain the deformation of a

B-spline generic head model. Pighin et al. (Pighin

et al., 1998) combine 2D morphing with 3D transfor-

mations of the geometric model to produce photore-

alistic facial animation. In addition, Blanz and Vetter

(Blanz and Vetter, 1999) present a process for esti-

mating the shape of a face in a single photograph, and

a set of controls for intuitive manipulation of appear-

ance attributes (thin/fat, feminine/masculine).

Texturing in the context of face modeling is, how-

ever, an often neglected issue. Williams (Williams,

1990) presents an approach to generate and register

a texture map from a peripheral photograph. This ap-

proach is meanwhile superseded by the ability of laser

scanners to acquire geometry and texture in one step

(Lee et al., 1995; Waters and Terzopoulos, 1991). The

method presented in (Blanz and Vetter, 1999) gener-

ates an individual face geometry and texture by linear

combination of face geometries and textures from a

large database. Marschner et al. (Marschner et al.,

2000) describe a technique that uses several input

photographs taken under controlled illumination with

known camera and light source locations to generate

an albedo texture map of the human face along with

the parameters of a BRDF.

Several image-based approaches (Akimoto et al.,

1993; Guenter et al., 1998; Lee and Thalmann, 2000;

Liu et al., 2001; Pighin et al., 1998) use a small num-

ber of input photographs (or video streams) for the re-

construction of both geometry and texture. Although

they could potentially yield a higher texture quality

compared to the scanned textures, they typically suf-

fer from a less accurate geometry reconstruction and

limited animation. Generating high-resolution tex-

tures for facial skin and eyes and teeth from several

uncalibrated photographs of a person’s head has been

addressed in (Tarini et al., 2002). In their method, the

geometry of the head is acquired using a structured-

light range scanner. The photographs are registered

and combined into a single texture suitable for mip-

mapping based on the multiple textures blending tech-

nique (Rocchini et al., 1999). In contrast, not requir-

ing separate camera setups, we synthesize a full-head

texture from a reﬂectance image acquired by a laser

scanner which captures color of only the face area.

3FACEDATA

We use a database that contains laser scans of 186 hu-

man faces (126 male and 60 female). Each subject is

captured wearing a bathing cap and with a neutral ex-

pression (see Fig. 2). The laser scans provide face

structure data (see Fig. 1 (a)) and RGB-color val-

ues that are stored in a 360× 524 image with 8 bit

per channel (see Fig. 1 (b)). The image is registered

against the range data and can be used for texture-

mapping (see Fig. 1 (c)). We use a generic model

created with Alias|Wavefront to resemble an average

human head. It consists of 2,632 vertices and 5,221

triangles. Prescribed colors are added to each triangle

to form a smooth-shaded surface (see Fig. 1 (d)).

We have interactively speciﬁed 83 feature points on

scanned face geometry. Our generic model is already

tagged with the same feature points by default. The

feature points are located around the eyes, eyebrows,

nose, mouth and chin (see Fig. 1). In the following,

the feature points on the generic head model are de-

GRAPP 2006 - COMPUTER GRAPHICS THEORY AND APPLICATIONS

268

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

Figure 1: Face data: (a) scanned face geometry; (b) acquired color image; (c) texture-mapped face scan; (d) and (e) feature

points speciﬁed on the generic model and face scan, respectively.

Figure 2: Some exemplar 3D data of the face database.

noted as the source feature points, whereas those on

the scanned surface are called target feature points.

4 MODEL ADAPTATION

4.1 Global Warping

The global warping is based on manually speciﬁed

corresponding feature point on the generic model and

the scanned data. Denote two sets of correspond-

ing 3D points given by s

and t

(i =1,...,n)as

the source and target feature points, respectively. We

need to ﬁnd a function f that maps the s

to the t

= f(s

) i =1,...,n (1)

The goal is to construct a smooth interpolating

function that expresses the deformation of the non-

feature vertices in terms of the changes in the feature

points during morphing. This problem is addressed

by scattered data interpolation methods. Radial Basis

Functions (RBFs) are a popular means of scattered

data interpolation. The interpolant using RBFs is a

function that utilizes the known displacement of each

feature point and returns the displacement value for

each non-feature point that takes it from the original

position to its position in the target form. Such a map-

ping can be expressed by a weighted linear combi-

nation of n basic functions φ

deﬁned by the source

feature points and an explicit afﬁne transformation:

f(s)=

i=1

(s − s

)+Rs + t (2)

where s ∈R

is a vertex on the generic model, c

∈

are (unknown) weights, ·denotes Euclidean

distance, R ∈R

3×3

adds rotation, skew, and scaling,

and t ∈R

is a translation component. Many differ-

ent functions for φ(r) have been proposed (Nielson,

1993). We evaluated several functions including the

multi-quadric φ(r)=



+ ρ

, the thin-plate spline

φ(r)=r

log(r), the Gaussian φ(r)=exp(−ρr

and the biharmonic φ(r)=r. We had better results

visually with the multi-quadric function.

To remove afﬁne contributions from the weighted

sum of the basic functions, we include the additional

constraints

i=1

=0,

i=1

=0 (3)

The system of linear equations (Eq. 1 and 3) is solved

for the unknowns R, t, and c

using a standard LU

decomposition with pivoting, to obtain the ﬁnal warp

function f. This function can then be used to trans-

form a vertex s in the volume spanned by the feature

points. Fig. 3 (a) shows the shape of the head model

after having interpolated the 3D displacements at 83

feature points and applied them to the entire head.

(a) (b) (c)

Figure 3: Adaptation of the generic model to scanned data:

(a) source model after global warping; (b) ﬁnal mesh geom-

etry after local deformation; (c) textured.

4.2 Local Deformation

The global warping with a small set of initial corre-

spondences does not produce a perfect surface match.

We further improve the shape using a local deforma-

tion which ensures that all the generic mesh vertices

are truly embedded in the scanned surface. The local

deformation is based on the closest points on the sur-

faces of the generic model and the scanned data. The

polygons of the generic model are displaced towards

their closest positions on the surface of the scanned

data. The polygons of the scanned data are organized

into a Binary Space Partition tree in order to speed up

the process of the closest point identiﬁcation.

AN EFFICIENT TEXTURE GENERATION TECHNIQUE FOR HUMAN HEAD CLONING AND MORPHING

269

After the model ﬁtting, texture from the reﬂectance

image is applied to the deformed generic model. As

each vertex on the ﬁtted generic mesh samples a

scanned point, it takes the texture coordinates asso-

ciated with that point. Fig. 3 (b) and (c) show the

results of smoothly shading and texture mapping.

5 TEXTURING A HEAD

5.1 Mesh Parameterization

Our head skin texture is generated from the acquired

color image, as shown in Fig. 1 (b). One of our

goals is to readily generate 2D texture metamorpho-

sis for head morphing. In general, morphing be-

tween two images requires pairwise correspondences

between image features. In our case, however, cor-

respondences between the two textures are implicit

in the texture coordinates of the two associated face

meshes. Since every face generated from one generic

model has a similar characteristic for texture coordi-

nates, we can produce shape free face texture images

by constructing a parameterization of the 3D generic

mesh over a 2D image plane.

(a) (b)

Figure 4: (a) Textured adapted generic model. (b) Texture

transferred to the undeformed generic model.

Given the vertex-wise correspondence between the

adapted generic head mesh and the original unde-

formed generic mesh, it is trivial to transfer a texture

map between this pair of meshes. Each vertex on the

original generic mesh simply takes the texture coordi-

nates of its corresponding vertex on the adapted mesh.

Fig. 4 shows the texture transferred from the adapted

generic model onto the original undeformed mesh.

We want to parameterize the 3D generic head mesh

over a 2D domain [0, 1]

in order to obtain a sin-

gle texture map for the whole mesh. We implement a

cylindrical projection which is preferable for regions

corresponding to a volumetric surface. A cylindrical

triangular mesh corresponding to the generic model is

constructed on a virtual cylinder enclosing the model.

The projection results in a cylindrical face mesh (see

Fig. 5 (a)). Each vertex of the 2D cylindrical mesh has

cylindrical coordinates with corresponding longitude

(0-360 degrees) along the x-axis and vertical height

along the y-axis. The mapping of world coordinates

x =(x, y, z) to 2D cylindrical coordinates (u, v) uses

u =tan

−1

(

),v= y (4)

The resulting (u, v) coordinates map to a suitable as-

pect and resolution image (512×512 in our experi-

ment). We also map the original generic mesh ren-

dered with transferred texture to the image plane us-

ing the same cylindrical projection. The result is

a 512×512 cylindrical texture image in which each

pixel value represents the surface color of the texture-

mapped face surface in cylindrical coordinates (see

Fig. 5 (b)). The generic face mesh can be textured

by this cylindrical texture image using normalized 2D

cylindrical coordinates as the texture coordinates.

(a) (b)

Figure 5: (a) Texture mesh parameterization. (b) Cylindri-

cal texture image.

5.2 Synthesizing A Full-Head Skin

Texture

After having created the 2D texture mesh from the

3D generic head mesh, we perform a vertex-to-image

binding for all vertices of the 3D head mesh. This

step is carried out by taking into account removal of

undesired textures of cap and dark hair. A vertex on

the generic mesh is bound to the input image, if it

samples the scanned surface and takes valid texture

coordinates of the sampling point in the model adap-

tation procedure. Removal of cap and hair textures is

done by unbinding the vertices with a color too dis-

similar to the color of the forehead. We compute the

average color and standard deviation of the vertices in

the forehead and unbind those vertices that are at least

λ times the standard deviation away from the average.

The parameter λ should be chosen within [1.5,3], as

it empirically proved to remove the problematic (cap

and hair) textures in most cases. Fig. 6 shows the re-

maining texture with 2D mesh parameterization and

its vertex binding visualized with color coding.

Let  =(x

, x

) denote a triangle of the face

mesh and

 =(

) be the corresponding tri-

angle in the texture mesh. For each triangle , one of

the following situations might occur (see Fig. 6 (b)):

1. There is a texture patch of the input image that can

be mapped to  (red triangles).

GRAPP 2006 - COMPUTER GRAPHICS THEORY AND APPLICATIONS

270

(a)

(b)

Figure 6: (a) The resulting cylindrical texture image after

cap and hair textures have been removed automatically. (b)

Color-coded triangles of the texture mesh: each red triangle

has all of its vertices bound to the input color image; blue

triangles have at lease one bound vertex and one unbound

vertex; the vertices of green triangles are all unbound.

2. Only one or two vertices of  are bound to the in-

put image (blue triangles).

3. No vertex of  is bound to the input image (green

triangles).

In the ﬁrst case, we rasterize

 in texture space.

For each texel T

, we color it with the color of the

image pixel P

that corresponds to T

. In the second

case, we color vertices of

 that are bound to the in-

put image with the pixel colors of the corresponding

pixels simply as in the ﬁrst case. For each unbound

vertex

, we check the vertices in its one-ring neigh-

bors that are colored by being bound to the input im-

age.

is then colored by summing up the weighted

colors of all the colored vertices

around it.

i=1

cos(

max

)C(

)

i=1

cos(

max

)

(5)

where n is the number of colored neighboring ver-

tices, d

are lengthes of the edges linking between x

and x

in the original 3D generic mesh, and d

max

the maximal edge length in the generic mesh. The

weight term measures the normalized distance be-

tween two vertices, and favors the vertices that are

much closer to the considered vertex. With the vertex-

to-image binding information, the texels of the raster-

ization of

 can be grouped into two sets: T

and T

Textured texel set T

represents the set of texels that

have a corresponding pixel in the input image. We

thus color this set of texels with their corresponding

pixel colors. If a texel T

has no corresponding pixel,

it is categorized into the colored texel set T

. For each

∈ T

, we determine its barycentric coordinates

(α

,β

,γ

) w.r.t.

 and compute the corresponding

color C(T

) by interpolating the vertex colors of

:

C(T

)=α

)+β

)+γ

) (6)

In the last case, all vertices of

 are unbound to

the input image and can not be colored by any of the

previously described schemes. This problem is ad-

dressed in a two-stage process: First, we iteratively

assign an interpolated color to each unbound vertex.

We then perform the color interpolation scheme for

the remaining triangles of

 that have not been col-

ored. The ﬁrst step iteratively loops over all unbound

and uncolored vertices of the 2D texture mesh. For

each unbound vertex

x, we check if the vertices in the

one-ring around

x are colored (either by being bound

to the input image or by having an interpolated color).

If this is true, we assign to

x the weighted sum of

colors of all the colored vertices around

x using Eq.

5, otherwise we continue with the next unbound ver-

tex. We repeat this procedure until there are no fur-

ther vertex updates. After this step, the ﬁrst round of

vertices connecting to the vertices in case 2 has been

colored. Next, we start the same procedure iteratively.

At each iteration we color new round of vertices ad-

jacent to the round of vertices colored in the last it-

eration. Upon termination of this loop, all vertices of

the texture mesh are either bound or colored and the

remaining triangles of

 can be colored using the in-

terpolation scheme from the second case.

Figure 7: Synthesized cylindrical full-head texture.

Since the input image has been acquired under un-

controlled illumination, the skin color might differ

noticeably between the left and right sides of the face.

In this case, boundary might appear at the intersec-

tion between the sagittal plane (vertical plane cutting

through the center of the face) and back of the head.

We apply a weighted average spline method (Peleg,

1981) to remove the visual boundary. Fig. 7 shows

the generated full-head texture after having synthe-

sized from the remaining face texture and applied the

weighted average spline to smoothly blend pixel col-

ors in the leftmost and rightmost texture regions.

5.3 Texturing Ears

The ears have an intricate geometry with many folds

and fail to project without overlap on a cylinder. Nev-

ertheless, it is possible to automatically and quickly

AN EFFICIENT TEXTURE GENERATION TECHNIQUE FOR HUMAN HEAD CLONING AND MORPHING

271

generate the texture from a single input image where

the ears are clearly visible.

We use a deformation technique based on feature

points to warp the reference ear model to an individ-

ual ear model for obtaining appropriate texture coor-

dinates. We use the acquired 2D image that contains

the individual ears as the target model (see Fig. 1 (b)).

We identify a small set of feature points in the input

image. In practice, we use fourteen feature points for

each ear, see Fig. 8 (a). As illustrated in Fig. 8 (b), our

generic model is tagged with the same feature points

by default. To segment ears, we predeﬁne a bounding

box enclosing each ear for the generic mesh (see Fig.

8 (d)). Before ﬁtting the reference ears to the target

shape, we need to transform positions of the reference

ear model into the coordinate system of the target ear

image. The segmented ears are transformed and pro-

jected onto the 2D image plane of the target ear image

(see Fig. 8 (e)). For the target feature points in the

input image, they can be easily detected due to their

distinct color and their image positions are calculated.

(a) (b) (c)

(d) (e)

Figure 8: (a) and (b) Position of feature points in the input

2D image and 3D generic model, respectively. (c) Ear fea-

ture points. Blue ones are used for the global alignment.

(d) Bounding box around a ear. (e) Projected reference ear

meshes with feature points.

Given two sets of N corresponding source feature

points p

and target feature points p

∗

in the image

plane, we ﬁt the projected reference ear mesh F to

the target ear shape in the input image F

∗

in a global-

to-local fashion. Our ear ﬁtting process consists of

global alignment and local adaptation. We use a sub-

set consisting of ﬁve features points for the global

alignment (see Fig. 8 (c)). The center of F, p

, is de-

ﬁned as the midpoint between p

and p

which is the

midpoint between p

and p

. The center of F

∗

, p

∗c

is calculated in the same way. Let an arbitrary ver-

tex x ∈ R

on F move to its new position x



∈ R

S ∈ R

2×2

be the scaling matrix, R ∈ R

2×2

be the

rotation matrix, and T ∈ R

be the translation vector.

Eq. 7 computes the transformation:



= SR(x − p

)+T (7)

In principle, ﬁve parameters must be estimated, the

in-plane rotation angle r, two scaling factors (s

and

) and two translation components (t

and t

) along

the u and v texture coordinate axes. The rotation an-

gle r is estimated as the angle between vectors

−−→

and

−−→

∗

. The scaling factors are estimated from the

ratio of lengths between a pair of feature points as

measured both in F and F

∗

 p

∗

− p

∗m



 p

− p



 p

∗

− p

∗



 p

− p



(8)

The translation vector is estimated by matching the

model center of F with that of F

∗

. The same trans-

formation is applied to the source feature points p

get their new positions p



for further local adaptation.

Fig. 9 (a) shows the global alignment results.

Local adaptation involves non-rigid deformation of

the transformed 2D reference ear mesh F



to ﬁt the

target ear shape. Once we have computed a set of

2D coordinates for the transformed source feature

points p



, we use these values to deform the remain-

ing vertices on F



. We construct a smooth interpola-

tion function that gives the displacements between the

original point positions and the new adapted positions

for every vertex on F



. Let N be the number of fea-

ture points, w

be the sum of weights from all feature

points contributed to a ear mesh vertex x



, and l

the distance between the vertex x



and a feature point



. Eq. 9 computes the displacement applied to x



x

j=1

− l

(N − 1)

p

−µl

(9)

where µ is a decay factor determined by the ear size,

and p

is the displacement of feature point p



p

= p

∗

− p



j=1

(10)

Texture coordinates for all vertices on the reference

ear model are obtained by normalizing the newly

adapted vertex positions in the image plane to the do-

main [0, 1]

. Fig. 9 (b) shows the local adaptation re-

sults. In the ﬁnal rendering of the head, the ear parts

are textured in a separate process with the assigned

individual texture map shown in Fig. 1 (b).

6 RESULTS

Fig. 10 shows the ﬁnal texture-mapped head, where

the texture image shown in Fig. 7 is applied to the

adapted generic head model shown in Fig. 3 (a). The

scanned data in Fig. 1 has a nice shape, but dose not

have good shape and texture for back part and ears.

Through our method using deformation and texture

GRAPP 2006 - COMPUTER GRAPHICS THEORY AND APPLICATIONS

272

Figure 10: Different views of the head model rendered with the generated full-head texture.

Figure 11: Examples of texture-mapped heads of various people.

Figure 12: Dynamic morphing between two textured heads.

(a)

(b)

Figure 9: (a) Global alignment of the reference ear meshes

with smooth shading and texture mapping. Red and green

dots represent the transformed source feature points and de-

tected target feature points, respectively. (b) After the local

adaptation.

synthesis with a generic head, it has proper texture on

backside and ear part, which makes full rotation of a

head. We have used our method to generate full-head

textures for various individuals. Female and male in

different ethnic groups with different skin color at-

tributes are reconstructed and textured, as shown in

Fig. 11. Rendering of head models is performed in

real-time using OpenGL hardware (about 120 fps on a

2.4 GHz PC with an ATI FireGL X1 graphics board).

Our method establishes the necessary mapping be-

tween different face scans through a single, prototype

layout which enables us to morph between any two

reconstructed models. Together with the geometric

interpolation, we blend the associated textures. The

generated cylindrical full-head textures possess the

correspondence, enabling 2D texture metamorphosis.

We vary the morphing ratios to generate a dynamic

morphing between two models, as shown in Fig. 12.

In our method, the only interactive step is the ini-

tial identiﬁcation of corresponding feature points on

scanned face surface for model adaptation and in ac-

quired reﬂectance image for texturing ears. This step

takes about 12 minutes. The automated method is

then executed to generate a full-head texture. The

RBF calculation and the morphing of the generic

mesh are performed instantaneously, taking about 2

seconds on a 2.4 GHz Pentium 4. With a dataset

of 300k points, the local deformation process runs

for about 14 seconds. Computing a parameterization

of the head mesh (approx. 5k triangles) takes about

50ms. The texture synthesis process performs 30 iter-

ations in approx. one minute, additional spline blend-

ing takes about 40 seconds. Automatic ear ﬁtting

process runs fast, taking 0.8 seconds for ﬁtting two

AN EFFICIENT TEXTURE GENERATION TECHNIQUE FOR HUMAN HEAD CLONING AND MORPHING

273

ears. Given the scanned data, the whole process of

creating a full-head texture takes within 15 minutes.

7 CONCLUSION

We have presented a new method to effortlessly gen-

erate a full-head texture for photorealistic head mod-

eling. We deform a generic head mesh to ﬁt the par-

ticular face geometry using a scattered data interpola-

tion approach. We automatically parameterize the 3D

generic mesh over the 2D texture domain to establish

the mapping correspondence between different face

textures. We generate a full-head texture using color

interpolation for unbound areas and weighted average

splines for visual boundary removal. The individual

ear textures are efﬁciently created from a single in-

put image. Using our technique, we have been able to

generate photorealistic head models and natural look-

ing morphing with minimal manual efforts.

We would like to use an anatomy-based model de-

veloped in our previous work (Zhang et al., 2004b) as

the generic model. As it has been designed to pro-

duce real-time physically-based animation, the tex-

tured head models with the adapted anatomical struc-

ture can be animated immediately with the given mus-

cle parameters. We also plan to address the prob-

lem of non-injective mapping under the chin with

the cylindrical texture by assigning a separate texture

where that part is clearly visible to the region. Instead

of current mesh representation, independent generic

models of eyes will be used to enable rotations of the

eyeball and personal textures extracted from a photo-

graph will be applied to generic models to add indi-

vidual characteristics. Finally, automated reconstruc-

tion of hair texture from images is one of the future

challenges.

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