SKIN MODELING AND RENDERING BASED ON VISUAL

PERCEPTION

Azam Bastanfard, Nadia Magnenat Thalmann

MIRALab, CUI University of Geneva,24 rue du General Dufour,CH-1211 Geneva SWITZERLAND

Keywords: Skin rendering, Human perception, Modelling.

Abstract: Human skin modelling and rendering are affected with a variety of cues. These are including human visual

perception of skin texture and lighting. An attempt to mimic such attributes by computer is an aspiring goal

and challenging task. This paper proposes a novel algorithm with two techniques as a key solution capturing

such a variety of cues to skin appearance. The idea is to capture these two characteristics for skin rendering.

The first is the texture generation that developed in visual perception. The second is skin texture rendering.

These techniques discuss the skin noise simulation based on human perception theory and simulate skin

noise texture. Then, the skin is rendered with what we call the Bidirectional Reflectance Distribution

Function Texture Magnitude technique. The original contribution and advantages of this paper compared

with other proposed methods are simple to implement, reliable and their computations are fast enough for an

interactive environment. Experimental results demonstrate our approach for skin texture generation.

1 INTRODUCTION

Creating realistic rendering of human skin has an

endeavour in computer graphics for nearly three

decades (Nahas et al., 1990). Application of human

skin modelling and rendering are founded in

sophisticated computer interface, VR applications,

computer games, multimedia and in a broad variety

of production animation. Human visual system can

easily recognize skin with other materials.

Reproducing visual perception by computer is a

difficult task because of the complex and individual

texture of the skin and spatially varying reflectance

properties of skin.

Rendering human skin based on human visual

perception requires a handle on how to capture the

image space created by the source of variation. In

principle two main elements have to be considered

in skin texture rendering based on human vision and

skin reflectance. This paper shows how to simulate

skin accounting for these elements. Our approach is

motivated by a research on two studies.

The first is human visual perception and noise

importance and texture generation for simulating

skin texture. The second is the rendering skin texture

based on a new BRDF technique.

The underlying idea to know the noise

importance and its limit is useful to calculate ideal

performance as a benchmark for simulation and

rendering. Visual sensitivity is a product of two

factors that isolate visual process more easily (Malik

et al., 1990). These two factors contain observer

efficiency and equivalent noise. On the other hand

the ability to delineate characters skin in visual

world depends partly upon the perception of noise

consistency.

Perlin has determined a function, which uses an

interpolation to combine noise-generating functions

(Perlin, 1985). It has also been used to produce

natural textures. Finally we render skin texture based

on BRDF technique. In rendering phase generated

texture is applied to render skin that will be

discussed in section 4.2.

This paper organized as the followings. Section

2 reviews some of the prior researches in skin

texture modelling and rendering with some

discussions on its problem. Section 3 provides a

brief summary of human vision and skin texture

perception. Section 4, introduces our proposed

techniques for skin texture generation and its

reflectance. Some experimental results and

discussion are given in section 5. Finally, conclusion

and direction for future work are discussed in

section 6.

313

Bastanfard A. and Magnenat Thalmann N. (2006).

SKIN MODELING AND RENDERING BASED ON VISUAL PERCEPTION.

In Proceedings of the First International Conference on Computer Graphics Theory and Applications, pages 313-318

DOI: 10.5220/0001357503130318

 SciTePress

2 PRIOR WORKS AND

PROBLEMS

Human skin modelling and rendering are extremely

important in enhancing the realism of human

appearance. Varied models are used to investigation

on human skin and researchers discussed it from

different point of view (Freefberg,

1999)(Hiraoka,1993). For example there are

geometric models, physically based models and

biomechanical models using either particle system

or continues system. Ishii et al. (Ishii, 1993) also

generated human skin texture, based on optical

scattering in the skin layer with a 3D mesh structure

of the skin surface. They reproduced the change in

texture appearance for various scattering coefficients

in the skin.

Debevec et al. (Debevec, 2000) proposed a

technique to obtain the reflectance field of a face at

each point by rotating the light source around the

face. Hanrahan and Krueger (Hanrahan, 1993) have

modelled skin subsurface structure and photon

migration in the skin, and have simulated the

shading of skin colour. Jensen et al.(Jensen, 2001)

reproduced realistic facial images based on photon

mapping or the diffusion equation technique for

subsurface scattering.

Bastanfard et al (Bastanfard, 2004) proposed a

technique for skin cosmetics based on wrinkle

removal using inpainting.

Cula et al. (Cula, 2005) developed a texture

representation technique that counts for changes of

skin as imaging condition are varied. An E-cosmetic

function for digital images based on physics and

physiologically-based image processing was

proposed by Tsuruma et al. (Tsuruma, 2004). Other

extension and improvement have been reported in

(Marschner, 1997)(Ramamoorthi, 2001)(Sato,

1994).

A Dynamic wrinkle model and skin aging was

proposed by Wu and Thalmann (Wu, 1995). Another

approach has been proposed by Boissieux et al.

(Boissieux, 2000) to simulate skin aging and

wrinkles with cosmetics insight. In this method

texture mapping offers a good simulation of static

wrinkles, but constructing visually interesting bump

maps requires practice and artistic skills.

Although much work has been done in skin

rendering, perception based model on description of

skin does not exist because of the complexity. On

the other hand there are no methods in which the

texture image generates, then modified as a function

for skin rendering. In this paper, a texture

analysis/synthesis technique is used to change the

amount of texture spatially then a Bidirectional

reflectance distribution function texture magnitude is

proposed to rendering effect. The results

demonstrate realistic modelling and rendering of

skin texture.

The original contribution of this paper compared

with the previous mentioned work is given in terms

of following advantages. First the proposed

techniques are efficient in time complexity and

simple to implement. Second they are local. By local

we mean that we can generate different skin based

on local noise. Third they provide a simple way to

capture the skin surface details. Finally, the

computations are fast enough for an interactive

environment.

3 VISUAL PERCEPTION OF

TEXTURE

Modelling of human skin has been known as an

essential step of different problems. Based on this

need many attempts have been done in this regard.

On the other hand, the ability to delicate skin texture

in computer graphics and virtual worlds depends

partly upon the perception of textural consistency.

The theory of texture perception attribute pre-

attentive texture discrimination to differences in

first-order statistics of stimulus features such as

orientation, size and brightness of constituent

elements.

The reason to describe particular aspect of visual

perception like contrast is human vision sensitivity

factor due to achieve realism in computer graphics.

For instance a local contrast calculation produces

contrast image that describe the response of

ensemble of neuron to a particular spatial frequency

and orientation. Contrast energy E is the square of

the contrast function summed over the dimensions

along which the stimulus varies (Pelli, 1999). For

static two-dimensional stimuli, signal energy is

generated over space:

dxdyyxcE ),(

∫

(1)

The contrast function is normalized derivation of the

luminance function from the background level,

[

]

LLyxLyxc /),(),(

−

(2)

We call them the channel images as they

represent different channel of visual system.

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314

Therefore visual sensitivity can be proportioned into

observer efficiency and visual noise (Pelli, 1999). In

this paper we generate noise texture and new

technique on texture luminance to gain geometry of

skin texture.

4 OUR PROPOSED ALGORITHM

The quest for improving realism is always an

important goal of computer graphic and virtual

world. Therefore current models of skin appearance

are not sufficient to support the demands for fast

algorithms. One major difficulty in skin perceptual

appearance is the extreme complexity of real skin

that exhibit many subtle variations over its entire

surface. This section explores a new algorithm with

two techniques for modelling and rendering of skin

based on human perception. In this approach Perlin

method is used to generate a 2D skin texture. Then

we extract noise from texture by proposing BRDF

texture magnitude. Finally skin texture will be

generated. Figure 1 illustrates the steps of our

proposed algorithm.

Figure 1: General structure algorithm outline.

4.1 Skin Rendering

Over the last few years, many construction of BRDF

have been introduced in optics and in computer

graphics literature (Kautz, 2004)( Kobbelt,

1997)(Marschner, 1999). This section focuses on

rendering of skin texture. Our idea is derived from

the geometry of bidirectional reflectance diffusion

function BRDF and the concept of texture analysis/

synthesis in term of textures magnitude. In the

followings we will summarize the fundamental

definition and basic notation of BRDF, texture

magnitude function and finally introduce our

proposed technique.

4.2 Bidirectional Reflection

Distribution Function

The bidirectional reflectance distribution function

(BRDF) of a surface describes how light is scattered

at its surface. Its value measures the ratio of the

radiance L exiting the surface in a given direction to

the incident irradiance I a particular wavelength

from an incident solid angle

about a given

illumination direction. The BRDF denoted by

f is

according to figure 2, as the following;

()

iiiii

eer

eeiir

ωθφθ

λφθφθ

cos),(

),(

,,;, =

(3)

Where,

),(),(

,, eevreesrr

LLL

(4)

Is the reflected radiance which has two

components, one is the reflected radiance due to the

surface scattering

, and the other

component

is due to subsurface volume

scattering. Therefore it has five variables but its

domain is reduced somewhat by a symmetry called

reciprocity. Reciprocity stated that reversing the

light path doesn’t change the reflectance, that is

(

)

),,,,(,,;,

11222211

(5)

Figure 2: Geometry of surface reflection.

The units of BRDF are thus inverse steradians. It

has been widely adopted where

, are the

SKIN MODELING AND RENDERING BASED ON VISUAL PERCEPTION

315

incident zenith and azimuth angles and

, are

the corresponding reflection angles.

Intuitively the BRDF represents for each

incoming angle, the amount of light that is scattered

in each outgoing angle. For a Lambertian (perfectly

diffuse) surface, for example, the BRDF is constant,

and equal to

where

is the diffuse reflection

coefficient and the factor of

is necessary so that

the BRDF is correctly normalized. From this point

of view then for a diffused surface, skin texture can

be rendered by texture magnitude that will explain in

next section.

4.3 BRDF Texture Magnitude

Skin data collection is time consuming and tedious

task. Therefore we generate texture using Perlin

noise.

In this approach we do not need to have a lot of

skin sample. The high spatial sampling of the

generated texture used to determine the BRDF

inherently enables extraction of the BRDFTM.

Figure 3 illustrates 2D skin texture generated using

Perlin noise. For example this 500 × 500 pixel

textures has an average digital count for the R, G

and B channels of 252, 215 and 190, this is

proportional to the BRDF.

Figure 3: 2D skin generated texture.

We implement BRDF for texture magnitude be

computed by convolution of the BRDF texture.

∑∑

−

−−=

],[],[

],[

jyixhyxf

yxg

(6)

Where f[x,y] is the texture and h[x,y] is a

convolution kernel. The g[x,y] histograms use

BRDFs texture data.

Therefore we made a new fast and empirical

algorithm for skin texture rendering.

In this approach at first we make a texture using

Perlin method. Since the texture colour ratio will be

changed, we called it texture magnitude. Then we

convert texture magnitude to BRDF average. We

have colour dispersion when the RGB refraction

indices are different. The difference in colour

between the images correspond changes gives the

texture magnitude, which can create complex

shading.

The process of our algorithm yields skin images

with fine appearance in a simple way. In this method

we control the skin noise and skin surface, surface

variation by adjust the noise range in texture.

Therefore, by generating Perlin noise texture and

proposing BRDFTM technique we generated skin

surface based on visual perception. This modelling

and rendering of human skin is fast and it is

convenient for interactive environments.

5 EXPERIMENTAL RESULTS

In this section, we present some images from

implementation of our proposed technique. Figure 4

demonstrates a virtual human with our skin texture

generation algorithm.

We illustrate generated skin texture with near

view in figure 5. The skin texture parameter contains

252,215,190 for R, G, and B. The period of Perlin

noise in this image is 1.56 and limit of noise is 10.

Figure 6. illustrates skin texture on more details.

In this approach at first we make a texture,

change the colour ratio that we called it texture

magnitude. Then we convert texture magnitude to

BRDF average. Therefore by generating different

skin noise texture, skin surface will be generated.

How ever our approach is not the exact

rendering of human skin but it is fast and it is

convenient for interactive environments. Figures

show skin texture with more convincing appearance

by proposed method.

Figure 4: Proposed skin rendering.

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Figure 5: Skin texture.

Figure 6: Skin texture in more details.

6 CONCLUSION AND FUTURE

WORK

Modelling and rendering of human skin texture are

difficult task and challenging problem. Many

different approaches have been proposed for human

skin modelling and rendering. This paper presents a

new and effective skin texture rendering. Two

proposed techniques have been introduced one for

empirical texture modelling and other for the

rendering effects. The advantages of the proposed

technique over previous method are given as

follows. First, the proposed techniques are efficient

in time complexity, simple to implement, and

reliable in which they don’t need to collect lot of

data. Second they provide a simple way to capture

the geometrical details founded in generated texture

without any constraints and render them smoothly.

Finally their computations are fast enough for an

interactive environment. On the other hand the

definition of skin texture appearance is a difficult

task. It depends not only on the structure of skin but

also so many aspects of one’s life, including

climatic, Psychology, and other parameters.

Therefore for more realistic skin effect further

research effort is needed. We would like to extend

our algorithm based on spherical wavelets.

ACKNOWLEDGMENTS

Special thanks to Nedjma Cadi Yazli for preparing

the model.

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