State of the Art in Skinning Techniques for Articulated Deformable

Characters

Nadine Abu Rumman

and Marco Fratarcangeli

Sapienza University of Rome, Rome, Italy

Chalmers University of Technology, Gothenburg, Sweden

Keywords:

Character Animation, Skinning, Skeleton-Based Animation, Physics-Based Animation.

Abstract:

Skinning is an indispensable component of the content creation pipeline for character animation in the context

of feature ﬁlms, video games, and in the special effects industry. Skinning techniques deﬁne the deformation

of the character skin for every animation frame according to the current state of skeletal joints. In this state of

the art report, we focus on the existing research in the areas of skeleton-based deformation, volume preserving

techniques and physically based skinning methods. We also summarize the recent research in deformable

and soft bodies simulations for articulated characters, and discuss various geometric and examples-based

approaches.

1 INTRODUCTION

In character animation, skinning is the process of

deﬁning how the geometric surface of the charac-

ter deforms according to a function of the skeletal

poses. Creating believable and compelling skin de-

formations is one of the main challenges of animated

feature ﬁlms, computer games, and interactive appli-

cations. Traditionally, bone transformations describe

the position and orientation of the joints, and the skin

deformation is computed by linearly blending bone

transformations to the skin. This technique is called

“skeletal subspace deformation”, also known as lin-

ear blend skinning (LBS), and it has been described

for the ﬁrst time in (Magnenat-Thalmann et al., 1988).

However, such simple and linear blending to the bone

transformations cannot be expected to capture com-

plex deformations. Moreover, because the deforma-

tion is restricted to the subspace of the afﬁne trans-

formation of the bones, this method has problems

deforming the skin near joints due to volume loss

or the well-known “candy-wrapper” artefacts. By

replacing linear blending with a nonlinear blending

(dual quaternion skinning, (Kavan et al., 2007)), the

artefacts of LBS can be completely avoided. How-

ever, dual quaternion blending suffers from an un-

desirable joint-bulging artefact while bending, which

requires artistic manual work to be ﬁxed. Although

these methods achieve real-time performance, they

are purely kinematic lacking of secondary motion ef-

fects, such as jiggling motion of fatty tissues and con-

tact deformation effects. In contrast, by employing

a physically based method into the skinning process,

the believability of character motions is highly en-

hanced. Physics-based simulations manage to bring

skeleton-based animation beyond the purely kine-

matic approach by simulating secondary motions like

jiggling, volume preservation and contact deforma-

tion effects. These secondary motions enrich the vi-

sual experience of the animation and are essential for

creating appealing characters animation. Despite of-

fering such realistic effects, physically based simula-

tion is computationally demanding and complex, thus

it is usually avoided in interactive applications. Fur-

thermore, physically based methods require human

intervention to generate input data, which describe the

physical states, before the problem can be solved by

a computer program. In most cases, such preparation

is tedious and the artist must master the knowledge of

both the given software and the underlying physics of

the phenomenon. The key challenge of producing be-

lievable deformations is to satisfy the conﬂicting re-

quirements of real-time interactivity and believabil-

ity. Believability requires achieving sufﬁcient defor-

mation detail, which means capturing the full range

of desired effects, namely jiggling, volume preserva-

tion, muscle bulging and skin contact deformations.

Producing these deformations demand at least an or-

der of magnitude more computation time than current

interactive deformation systems.

200

Rumman, N. and Fratarcangeli, M.

State of the Art in Skinning Techniques for Articulated Deformable Characters.

DOI: 10.5220/0005720101980210

In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 1: GRAPP, pages 200-212

ISBN: 978-989-758-175-5

The aim of this paper is to provide a comprehen-

sive survey on the existing skinning techniques in lit-

erature. These can be classiﬁed into three categories:

skeleton-based deformation, volume preserving skin-

ning and physics-based skinning methods. We dis-

cuss the existing skinning methods and how they ad-

dress the above-mentioned problems, and we also

highlight the advantages and disadvantages of each

method. Skinning methods can be also classiﬁed into

smooth skinning, data-driven skinning, and physics-

based skinning.

2 SKELETON-BASED SKINNING

METHODS

The most common approach for deforming articu-

lated character’ skin is to deﬁne the surface geome-

try as a function of an underlying skeletal structure.

Due to the simplicity, intuitive manipulation, and the

ability to quickly solve inverse-kinematics on a small

subspace (i.e., the skeleton), skeleton-based methods

are very popular and widely used in the animation

industry (Le and Deng, 2012). When modeling a

skeleton-based deformation, the challenge is to ob-

tain high-quality skin deformations in real-time given

an arbitrary skeletal posture. The current skeleton-

based deformation techniques can be divided into two

sections: geometry-based methods (Section 2.1) and

example-based methods (Section 2.2).

2.1 Geometric Skinning Techniques

In geometric skinning techniques, skeleton-to-skin

binding is deﬁned in a direct, geometrical way. Ge-

ometric approaches to deform articulated characters

have shown reasonable results at interactive rates.

We start by discussing the standard real-time method

“skeletal subspace deformation”, also known as

linear blend skinning (LBS) presented in (Magnenat-

Thalmann et al., 1988). This method has been widely

adopted in real-time applications such as games, for

its computational efﬁciency and straightforward GPU

implementation. Unfortunately, linear blend skinning

suffers from visual artefacts like self intersection,

volume loss or the well-known “candy-wrapper”

artefact

(see Fig. 1), which are the result of the

linear nature of the algorithm, since the linear

interpolation of the transformation matrices is not

equivalent to the linear interpolation of their rotations

The “candy-wrapper” artefact is the skin collapsing

effect exhibited by linear blend skinning (Magnenat-

Thalmann et al., 1988).

(Alexa, 2002). The limitations of LBS have been

extensively studied, where many techniques have

been proposed to avoid its artefacts. One possibility

is to enrich the space of skinning weights, leading

to methods which are still linear but offer a wider

range of deformations. These methods are called

multi-linear skinning techniques (Wang and Phillips,

2002; Mohr and Gleicher, 2003), in which the

extra weights are learned from input examples and

regularization is used to prevent overﬁtting. Merry

et al. propose a multi-linear skinning model called

Animation Space (Merry et al., 2006), which uses 4

weights per vertex-bone pair. However, this increase

in the number of weights carries an additional cost

in time and space, as well as parameter passing.

While linear skinning techniques are popular due to

their efﬁcient implementations, they cannot entirely

remove the “candy-wrapper” artefact, which is in all

cases noticeable under large joint rotations. For a

comprehensive survey on linear skinning techniques,

we refer the interested reader to (Jacka et al., 2007).

Selecting good skinning weights is critical to avoid

the artefacts and generate more natural deformations.

Recently, an automatic computation of skinning

weights was presented in (Dionne and de Lasa,

2013). In their method, the inﬂuence weights are

determined using geodesic distances from each bone,

which makes the inverse-distance weights shape-

aware and can even work with production meshes that

may contain non-manifold geometry. Despite that

associating skinning weights with the mesh vertices

can be done automatically, this method tends to either

increase or decrease the volume around joints.

By replacing linear blending with nonlinear blend-

ing (Hejl, 2004; Kavan and Z

ara, 2005; Kavan

et al., 2007), the “candy-wrapper” artefact can be

completely avoided. Nonlinear skinning methods

convert the afﬁne rigid transformation matrices to

< quaternion,translation > pairs, which are then

easier to blend than their matrix equivalents (Hejl,

2004; Kavan and Z

ara, 2005). In (Hejl, 2004) some

constraints are imposed on the character’s rigging,

whereas spherical skinning (Kavan and Z

ara,

2005) uses a computationally expensive Singular

Value Decomposition (SVD) scheme. The practical

impact of these two methods is limited because

of their dealing with the translational component

of the skinning transformations. In contrast, dual

quaternion skinning (DQS (Kavan et al., 2007)) uses

an approximate blending technique based on dual

quaternions (essentially, two regular quaternions).

Dual quaternion skinning is able to achieve com-

parable speeds to LBS while increasing the visual

State of the Art in Skinning Techniques for Articulated Deformable Characters

201

Figure 1: Demonstrates the artefacts of classic interactive skinning techniques linear blend skinning (LBS) and dual quaternion

skinning (DQS). Linear blend skinning (LBS) is the most widely employed skinning technique, due to its simplicity and

efﬁciency. Unfortunately, LBS suffers from the “candy-wrapper” artefact while twisting ((a) and (b)). This artefact can be

eliminated by a nonlinear blending method such as dual quaternion skinning (DQS), but DQS produces an unnatural joint-

bulging artefact while bending (c). Observing that LBS does not produce bulging while bending and DQS does not suffer

from the “candy-wrapper” artefact while twisting. (Image taken from (Kavan and Sorkine, 2012)).

quality of the animation. However, it suffers from an

undesired joint bulging

artefact (as shown in Fig. 1),

which requires artistic manual work to be ﬁxed.

Manually ﬁxing these artefacts is a a tedious process,

so automatic skinning techniques are becoming

increasingly popular (Baran and Popovi

c, 2007;

Kavan et al., 2009; Chen et al., 2011; Jacobson et al.,

2011; Bharaj et al., 2012; Jacobson et al., 2012).

Moreover, an interesting extension of linear blend

skinning called spline-skinning comes from (Yang

et al., 2006; Forstmann and Ohya, 2006; Forstmann

et al., 2007), which often produces better skinning

deformations and suppresses (but not completely

eliminates) the “candy wrapper” artefact. Instead of

using conventional matrix rotations, spline-skinning

represents each bone of the skeleton by a spline.

Furthermore, an appealing extension of DQS that is

successfully applied in a production setting (Disney’s

Frozen), is depicted in (Lee et al., 2013). For a

extensive discussion on nonlinear skinning methods,

we refer the reader to (Kavan et al., 2009). Whilst

all the above-mentioned methods fully deﬁne the

surface positions based on skeletal conﬁguration,

they cannot capture secondary motion effects and

skin contact behavior in response to collision. Re-

cently, more advanced geometric skinning methods

were introduced to limit the artefacts of LBS, while

keeping their simplicity. Kavan and Sorkine (Kavan

and Sorkine, 2012) developed a new skinning method

based on the concept of joint-based deformers, which

avoids the artefacts of linear blend skinning as well

as the bulging artefact of dual quaternion skinning.

Jacobson et al. (Jacobson and Sorkine, 2011)

expanded skinning to support bending, stretching and

twisting by using a slight variation on the standard

skinning equations. Impressive skinning results

can be obtained using the technique presented in

Joint-bulging is an unnatural skin bulging effect pro-

duced by dual quaternion skinning (Kavan et al., 2007)

while bending.

(Vaillant et al., 2013), which generates visually plau-

sible skin deformations in real-time. Their method

automatically captures contact surfaces between

skin parts without requiring any collision detection

step. Moreover, they extended their framework to

handle local skin contacts and produce the effect of

skin elasticity (sliding effect) (Vaillant et al., 2014).

More recently, (Kim and Han, 2014) proposed a

post processing method for dual quaternion skinning,

which eliminates the joint-bulging artefacts and its

suitable for real-time character animation. In spite

of improvements, geometric skinning techniques

remains purely kinematic, lacking of secondary mo-

tions effects like passive jiggling motion of the fatty

tissues or muscle bulging. In the next section, we

present the most inﬂuential example-based skinning

methods which are able to alleviate the limitations of

geometric skinning method and add dynamic effects

to the skin. For a more thorough treatment of these

methods, we refer the reader to (Jacobson et al.,

2014).

2.2 Example-based Skinning Methods

This class of skinning methods can be also called

data-driven methods. In contrast to geometric ap-

proaches, example-based skinning methods permit

more complex skinning effects such as muscle bulges

and wrinkles, while also addressing the artefacts

of linear skinning techniques. These methods take

as input a series of sculpted example poses and

interpolate them to obtain the desired deformation.

One of the ﬁrst example-based methods is pose space

deformation (PSD, (Lewis et al., 2000)), which

uses a radial basis function to interpolate correction

vectors among the example poses. In pose space

deformation method, pose space is a set of degrees of

freedom of a character’s model which vary between

the example poses. A particular pose is a particular

conﬁguration of these degrees of freedom. A more

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202

Figure 2: A set of example poses from an anatomically motivated arm model with both bending and twisting at the elbow.

The twisting and muscle bulges are enough to prevent LBS from approximating the examples well. The technique of (Mohr

and Gleicher, 2003) does better, but still differ from the given example poses. The model from (Wang et al., 2007) well-

approximate the examples poses. (Image taken from (Wang et al., 2007)).

sophisticated extension of PSD was presented in

(Sloan et al., 2001). Their method interpolates an

articulated character using example poses scattered

in an abstract space. This abstract space consists

of dimensions describing global properties of the

3D character, such as age and gender, in addition

to dimensions that are used to describe the conﬁg-

uration, such as the amount of bend at the elbow

joint. Moreover, PSD was generalized to support

weight in a technique called weighted pose space

deformation (WPSD), in (Kurihara and Miyata,

2004; Rhee et al., 2006)), which largely reduces the

number of required example poses. Although WPSD

can handle large-scale deformations well, it cannot

provide detailed deformation and it requires more

computation than the original pose space deformation

(PSD). In example-based methods, the amount of

memory grows with the number of training examples,

thus they are more popular in animated feature ﬁlm

(DreamWorks Animation’s Shrek 2) than in real-time

application. To tackle this problem, (Kry et al., 2002)

proposed a method similar in spirit to PSD called

EigenSkin. Instead of using all the displacements

for example poses, they used precomputed principal

components of deformation inﬂuences on individual

joints. The resulting algorithm leads to considerable

memory savings and enables to transfer the compu-

tations to the GPU. Despite the fact that pose space

deformation methods are simple to implement, they

require tremendous effort from artists because they

have to create different poses by hand for a wide

variety of examples. Another class of example-based

methods, which is a direct generalization of LBS

but does not require data interpolation, is com-

posed by methods such as single-weight enveloping

(SWE, (Mohr and Gleicher, 2003)) and multi weight

enveloping (MWE, (Wang and Phillips, 2002)).

Single-weight enveloping estimated single weight

per vertex with rigid character bones, with provisions

made for adding additional bones. Multi-weight

enveloping, instead, is based on a linear framework

supporting multiple weights per vertex-bone, and

it provides better approximations than SWE but

at the cost of 12 weights per vertex-bone instead

of 1 weight per vertex-bone as in SWE. However,

linearity has certain beneﬁts: it is fast and it can be

used to derive a measure of average distance across

the space of poses but the example meshes are still

necessary in order to obtain the weights. This class

of methods allows a smaller number of poses to be

used to generate a larger number of deformations. On

the other hand, they introduce an increased number

of weight parameters which must be computed in a

complex process.

As an alternative to using sculpted example poses,

several example-based approaches use scanned or

photographed data. Early work that uses 3D scanned

poses of a human body in character skinning has

been presented in (Min et al., 2000). Additionally,

the method in (Allen et al., 2002) creates a high

quality posable upper body model from range scan

data and markers. In their method, they obtain

deformations corresponding to different poses by

matching a subdivision surface template to the

range data. Recently, more advanced example-based

techniques have been effectively integrated with

mesh deformation algorithms to further improve

the quality of skinning (Wang et al., 2007; Shi

et al., 2008; Schumacher et al., 2012; Le and Deng,

2014). A rotational regression model is proposed

in (Wang et al., 2007), which captures common

State of the Art in Skinning Techniques for Articulated Deformable Characters

203

skinning deformation such as muscle bulging (as

we can see in Fig. 2) and twisting, speciﬁcally in

challenging regions such as the shoulders. Park and

Hodgins also introduced an interesting technique that

captures and synthesizes detailed skin deformations

such as bulging and jiggling (Park and Hodgins,

2006; Park and Hodgins, 2008), when a character

performs dynamic activities. They use a very dense

and large set of markers to capture the dynamic

motions. Then, they employ a second-order skinning

scheme followed by a radial basis function of the

residual errors to provide detailed skin deformations.

While high-quality skin deformations can be captured

accurately using scanned data, marker-based motion

capture systems typically have a time-consuming

calibration process and signiﬁcant hardware and

set-up costs. Example-based skinning methods are

attractive because they can provide rich details from

physical measurements and add realistic secondary

deformation to the skeleton-based animations. Shi

et al. presented an appealing method that is able to

provide the jiggling of the fatty tissues in real-time

by taking a surface mesh and a few sample sequences

of its physical behavior (Shi et al., 2008). Moreover,

the method proposed in (Huang et al., 2006) is

capable of synthesizing high resolution hand mesh

deformation with rich and varying details, from only

14 examples poses. However, these approaches do

not capture detailed soft-tissue deformations on a

wide variety of body shapes. The major drawback

of example-based methods is the need for example

poses. Besides the fact that when the example poses

cannot be captured on a real actor, creating these

poses requires either tremendous effort from an artist,

or a complex physical simulation on a volumetric

version of the skin mesh. In both cases, the mesh

and its associated skeleton at rest are not sufﬁcient,

and further human intervention is required. An

interesting discussion on example-based deformation

methods, can be found in (Feng et al., 2008). In the

next section, we discuss several volume preservation

methods for skinned characters, which have been

proposed to tackle the loss of volume artefact of

linear skinning techniques.

3 VOLUME PRESERVING

SKINNING METHODS

Volume preservation is an important aspect in the

context of skin deformation that has been addressed

in a variety of research papers over the last years.

Volume preservation methods allow artists to correct

the volume changes through the generation of extra

bulges and/or wrinkles. The method that has been

proposed in (Desbrun and Gascuel, 1995) is one

of the ﬁrst methods to introduce volume preserving

deformation, where they use local volume controllers

to guarantee volume conservation of implicitly de-

scribed soft substances. Moreover, multi-resolution

methods (Guskov et al., 1999; Botsch and Kobbelt,

2003) can preserve surface details by decomposing

a mesh into several frequency bands. Furthermore,

Funck et al. presented an appealing approach that

deforms the mesh vertices based on vector ﬁeld

integration (von Funck et al., 2006; von Funck et al.,

2008). However, these two methods are either com-

putationally expensive or do not ﬁt into the standard

animation pipeline. Angelidis and Singh developed a

skinning algorithm based on a powerful embedding

into the volumetric space, which enables to preserve

volume locally and globally (Angelidis and Singh,

2007). In their method, a degree of freedom is left

to the artist to control the ﬁnal shape, although its

combination with skinning weights variation along

the mesh makes this control somewhat indirect.

Recently, (Rohmer et al., 2008; Rohmer et al., 2009)

presented an automatic volume correction method to

model the constant volume behavior of soft tissues.

It corrects the resultant deformations of LBS using

a set of local deformations. In their work, they used

an automatic way to segment an organic shape into a

set of regions corresponding to the main muscle and

fatty tissue areas, in which volume is computed and

locally corrected. Huang et al. employed a nonlinear

version of the volumetric graph Laplacian, which

features nonlinear volume preservation constraints

(Huang et al., 2006). Lipman et al. introduce a

shape and volume preserving mesh editing technique

(Lipman et al., 2007a), where meshes are represented

by moving frames. These frames are scaled during

deformation such that the volumetric shape properties

are preserved.

Several impressive works that create an inner

scaffolding of spring, which resist compression to

maintain volume are described in (Hong et al., 2006;

Zhou et al., 2005). The method in (Zhou et al., 2005)

provides an excellent introduction to these interior

lattice methods. Lattice-Based freeform deformation

(FFD) are widely-used in commercial software

(such as Autodesk 3D Studio Max and Maya) for

providing smooth deformations and preserving the

volume of the skin (Sederberg and Parry, 1986;

Bloor and Wilson, 1990; Coquillart, 1990; Milliron

et al., 2002). For example, Autodesk Maya 2007

supports the notion of ﬂexors. This lattice ﬂexor

uses a local FFD lattice, which can then be driven

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204

Figure 3: Skinning with cage: (a) Input geometry with skeleton. (b) An initial cage constructed from four templates, which

are associated with the hand joint, elbow joint, upper arm bone, and the shoulder joint. (c) The skeleton deforms the mesh

templates. (d) The geometry is deformed by the cage, yielding a non-pinching elbow and muscle bulging. (Image taken from

(Ju et al., 2008)).

by joint transformations. However, the ﬂexors do not

support skinning transfer and the use of ﬂexors can

require signiﬁcant setup and tweaking because of the

multitude of lattice points. FFD was ﬁrst formally

proposed in (Sederberg and Parry, 1986) both as a

representation for free-form solids and as a method

for sculpturing solid models. Using FFD, a complex

character can be deformed by positioning the control

vertices of the coarse control grid. A more general

extension of FFD (EFFD) was later presented by

(Coquillart, 1990). Although lattice-based methods

give the artist the ﬂexibility of creating the desired

deformation, they require additional setup work and

the deformation is sometimes difﬁcult to predict.

On the other hand, cage-based skinning tech-

niques consider an appealing way to control the

deformation of an enclosed ﬁne-detailed mesh and

help to preserve the volume of skin deformations

(Ju et al., 2005; Joshi et al., 2007; Lipman et al.,

2007b; Ju et al., 2008; Savoye and Franco, 2010).

Cage-based techniques can be considered as a gener-

alization of the lattice-based freeform deformation.

Instead of a regular control lattice, a cage is deﬁned

by a ﬁxed-topology control lattice that is ﬁtted

to the character skin. The cage can be seen as a

low-resolution abstraction of the character, which

enables the user to deform a character using a simpler

mesh. Most cage-based deformation methods are

special case of linear blend skinning, where the

handle (cage vertex) transformations are restricted

to be translations and the focus is on choosing the

weights. The method presented in (Ju et al., 2008)

uses cage-based deformations to implement skinning

templates, which offer a ﬂexible design space within

which to develop reusable skinning behavior. In

their method, the skeleton drives the motion of

the cage vertices using an example-based skinning

technique, where the cage smoothly deforms the

character model (see Fig. 3). Joshi et al. proposed

a powerful cage-based deformation method based

on harmonic coordinates (Joshi et al., 2007) for use

in high-end character articulation. Their technique

guarantees that the inﬂuence of each cage vertex is

non-negative and falls off with distance as measured

within the cage. It generates a pleasing deformation,

but computing the harmonic coordinates is not easy.

In spite of that, cage-based techniques allow smooth

deformation of skin geometry. Posing the cage

requires manual manipulation of the cage vertices.

For an overview discussion on volume-preserving

deformation methods, we refer the reader to (Nieto

and Susin, 2013). Another promising way to preserve

the volume of the skin and to achieve realistic

deformation is by applying physics-based simulation

into the skin layer around the skeleton. The following

section describes the vast literature on physics-based

methods.

4 PHYSICS-BASED METHODS

In order to model dynamic phenomena, such as the

vibration of fatty tissues, muscles bulging and skin

contact deformations due to collisions, the animator

must conﬁgure the deformation for each keyframe.

While manually posing a character for each anima-

tion keyframe allows artists to create such realistic

effects, this process is tedious. Therefore, an alter-

native method is to employ physics into the skinning

process, which highly enhances the believability and

realism of character motions. Accordingly, physics-

based simulation manages to bring skeleton-driven

animation beyond the purely kinematic approach by

simulating secondary motions, such as jiggling of soft

tissues when the character is moving. Those sec-

ondary motions enrich the visual experience of the an-

imation and are essential for creating appealing char-

acters for movie productions and virtual reality ap-

plications. After the pioneering work of Terzopou-

State of the Art in Skinning Techniques for Articulated Deformable Characters

205

los et al. (Terzopoulos et al., 1987) and concurrently

Lasseter’s animation principle “squash and stretch”

(Lasseter, 1987). Physical simulation has taken an im-

portant role in the animated feature ﬁlm industry and

computer games (Goktekin et al., 2007), where many

physically based methods encouraged to simulate soft

bodies or add dynamic effects to the skin. In the fol-

lowing subsections, we ﬁrst discuss soft body simu-

lations in (Section 4.1), and then physically based

skinning methods in (Section 4.2).

4.1 Deformable and Soft Bodies

Simulations

Simulating soft bodies can be achieved in different

ways, and the design choice often has to balance

the required accuracy and performance (Moore and

Molloy, 2007). The most popular techniques for

simulating soft bodies in computer animation are

force-based methods. In particular, most of the tech-

niques used to simulate dynamics rely on mass spring

systems, because of the simplicity and efﬁciency.

The general idea is to represent the vertices of the

mesh as mass points, governed by Newton’s second

law of motion, and the edges as elastic massless

links (spring). Hence, the mesh is deformed when

the lengths of the elastic links change. This happens

when the relative position of the mass points changes

due to external forces. Mass-spring systems are

based on a local description of the material, in which

the physics of such systems is straightforward and

the simulator is easy to implement. However, to

simulate a particular material, it is important to select

carefully the parameters of the springs, such as the

stiffness and damping. Despite that these systems

are fairly easy to implement, they suffer from insta-

bility and overshooting problems under large time

steps. Moreover, mass-spring systems are often not

accurate, since they are strongly topology dependent

and are not built based on elasticity theory. On the

other hand, ﬁnite element methods (FEM) allows to

model elastic materials, in which both the masses

and the internal and external forces are lumped to

the vertices. The vertices in the mesh are treated

like mass points in a mass spring system while each

element acts like a generalized spring connecting

all adjacent mass points. The methods presented in

(Terzopoulos et al., 1987; Chadwick et al., 1989) are

the ﬁrst to demonstrate the effectiveness of compara-

tively simple mass-spring based approaches. In their

methods, they applied the Lagrangian equations of

motion using a ﬁnite difference scheme to simulate

elastic objects with regular parametrizations. Here,

the physical material properties can be described

using only few parameters that are used to model soft

bodies in an accurate manner. Unlike mass-spring

systems, ﬁnite element methods are easy to simulate

for any particular material. This makes things easier

for artists in charge of modelling different types of

soft bodies. Unfortunately, ﬁnite element methods

are avoided in real-time applications, because they

are computationally expensive and complex to

implement. Various methods have been proposed to

address the drawbacks of mass-spring systems and

ﬁnite element methods (Gourret et al., 1989; Turner

and Thalmann, 1993; Lee et al., 1995; Popovi

c et al.,

2003; Larboulette et al., 2005). A comprehensive

survey of Nealen et al. (Nealen et al., 2006) provides

the details about these techniques. The concept of

employing dynamic simulations into skinning for

the purpose of character animation was introduced

over two decades ago (Girard and Maciejewski,

1985), where many techniques were proposed to

reduce the accuracy of the simulation to help improve

performance and interactivity. Capell et al. (Capell

et al., 2002) used a volumetric ﬁnite element mesh

to represent the deformation of skin, driven by the

underlying skeleton motion. They extended their

method to include rigging forces, which guide the

deformation to a desired shape (Capell et al., 2005).

In their method, they effectively handled the effect

of skin movement by using skeletal constraints, but

by using forces that can violate the conservation of

momentum makes their simulation unstable under

large time steps. Shinar et al. (Shinar et al., 2008)

presented a framework of a two-way coupling be-

tween rigid and deformable bodies, in which they use

a time integration scheme for solving dynamic elastic

deformations in soft bodies interacting with rigid

bodies. However, their method does not facilitate

the development of an interactive animation system,

because of the massive computation required for the

ﬁnite elements representing the deformable body. In

contrast, a possible way to accelerate the simulation

of soft bodies is to focus on the surface rather than the

volume (Bro-nielsen and Cotin, 1996). In particular,

Galoppo et al. (Galoppo et al., 2007) presented a

fast method to compute the skin deformation on the

surface of a soft body with rigid core. Their formula-

tion only considers the elastic energy from skin-layer

deformation, and does not include the deformation

inside the volume. This may lead to inaccuracies

when capturing pose-dependent deformations. All

the above methods are only valid for small deforma-

tions and are unsuitable for an articulated character’s

large deformations. On the other hand, M

uller et

al. (M

uller et al., 2002) achieved a good real-time

performances for large rotational deformations,

GRAPP 2016 - International Conference on Computer Graphics Theory and Applications

206

Figure 4: Employing dynamic simulation into skinning process allows two-way interactions between the skeleton, the skin

geometry, and the environment at interactive rates. (Image taken from (Liu et al., 2013)).

by using a pre-computed linear stiffness matrix to

generate the deformations; their method is simple

and rotationally invariant. Recently, Kim and Pollard

(Kim and Pollard, 2011) proposed an approach

relying on ﬁnite element method to simulate the skin

deformation, able to handle both one-way and two-

way simulations. Their method generates compelling

dynamic effects and the deformations are obtained at

near interactive rate. The method proposed in (Gilles

et al., 2011) simulates dynamic skinned deformation

models using frame-based degrees of freedom with

unreduced force evaluation. Jain and Liu presented a

robust approach that realistically simulate characters

with soft tissue at the site of contact, where they used

two-way coupling between articulated rigid bodies

and deformable objects (Jain and Liu, 2011). Liu

et al. developed a framework that simulates and

controls skeleton driven soft body characters (Liu

et al., 2013). Their method couples the skeleton

dynamics and the soft body dynamics to enable

two way interactions between the skeleton, the skin

geometry, and the environment at interactive rates (as

we can see in Fig. 4).

4.2 Physically based Skinning

Physics-based methods are the natural choice for cre-

ating secondary motion effects such as ﬂesh jiggling

when a character is moving (Turner and Thalmann,

1993; Lee et al., 2009; Gilles et al., 2011; Saito

et al., 2015). Turner and Thalmann model the elas-

ticity of skin for character animation and simulate the

fat layer by Hookian spring forces (Turner and Thal-

mann, 1993). However, they treat muscles as purely

controlled elements. Thus, they do not model mus-

cles with deformable methods. Wilhelms (Wilhelms,

1994) presented an approach for animated animals

by simulating individual bones, muscles, soft tissues

and skin. The use of muscles, soft tissues and ﬂesh

elements makes it hard to ﬁt this approach into the

skinning framework. Moreover, Hahn et al. (Hahn

et al., 2012; Hahn et al., 2013) generated secondary

skin dynamics based on the rig degrees of freedom.

Their methods simulate the deformation of a charac-

ter’s fat and muscles in the nonlinear subspace in-

duced by the character rig. In the other direction,

Kim and James (Kim and James, 2011) proposed

a domain-decomposition method to simulate artic-

ulated deformable characters entirely within a sub-

space framework, where they combined locally ro-

tated nonlinear subspace models to simulate the de-

tailed deformations of the models. In order to simu-

late the musculotendons of the human hand and fore-

arm, (Sueda et al., 2008) add anatomic detail using

the tendons and bones. While physics-based skinning

methods can automatically generate secondary mo-

tion with high visual quality, they entail a signiﬁcant

computational burden that slows production and pro-

hibits its use in interactive environments. McAdams

et al. (McAdams et al., 2011) presented a robust

method using a uniform hexahedral lattice, which

provides convincing deformations of the skin with

contact handling. In addition, they introduce a one-

point quadrature scheme and a multi-grid solver in

order to improve the performance and stabilize the

simulation. Although their method can capture ap-

pealing skin deformations and guarantee pinch-free

geometry, it works at best at near interactive perfor-

mance (as we can see in Fig. 5). Recently, Deul and

Bender (Bender et al., 2013) introduced a multi-layer

character skinning based on shape matching with ori-

ented particles, used to simulate the elastic behav-

ior of a closed triangular mesh as a representation of

a skin model. They make a use of position-based

constraints for coupling the skeleton with the skin

and handling self-collisions. In (Rumman and Fratar-

cangeli, 2014; Rumman et al., 2015; Abu Rumman

and Fratarcangeli, 2015), a two-layered approach, the

skin is ﬁrst deformed with a classic linear blend skin-

ning and then the vertex positions are adjusted us-

ing Position-based Dynamics. This allows to robustly

mimic the behavior of the skin, and achieve and tune

effects like volume preservation and jiggling at inter-

State of the Art in Skinning Techniques for Articulated Deformable Characters

207

Figure 5: The Method of (McAdams et al., 2011) takes a skeleton and a surface mesh as input. Based on a hexahedral

lattice with 106,567 cells (center), their method simulates the deformed surface (right) obeying self-collision and volumetric

elasticity at 5.5 seconds per frame. (Image taken from (McAdams et al., 2011)).

active rate. Lastly, Gao et al. (Gao et al., 2014) pro-

posed a physics-based skinning method for skeleton-

based characters. They introduced a material model

of ex-rotated elasticity, which uses a procedural skin-

ning technique to approximate co-rotated elasticity

with an afﬁne force model and pose-dependent coefﬁ-

cients. Their method can simulate high-resolution hu-

man ﬂesh models with full external and self-collision

processing, without the ability to generate secondary

motions effects like jiggling of the fat tissues.

5 CONCLUSION

In this paper, we have reviewed the common tech-

niques for modelling deformations, especially those

for character animation purposes. A greater atten-

tion was paid to skeleton-based methods, and physics-

based methods. In skeleton-based skinning such as

linear blend skinning, dual quaternion skinning and

pose space deformation, surface deformation is re-

stricted to the skeletal pose that fully deﬁnes the sur-

face deformation. While skeleton-based methods can

produce good results, the believability of the deforma-

tion using these methods is limited. Because they can-

not capture secondary motions effects and skin con-

tact deformations. Skeleton-based deformation alone

is not sufﬁcient for capturing believable skin defor-

mations, such as skin stretching, secondary motion

effects and skin contact due to collisions. In contrast,

physics-based simulations bring skeleton-based ani-

mation beyond the purely kinematic approach by sim-

ulating secondary motions such as jiggling of soft tis-

sues when the character is moving, as well as captur-

ing skin contact deformation. Simulating such ﬂesh-

like deformations is difﬁcult due to the coupling be-

tween the skeleton and soft skin. Moreover, the re-

sultant deformation has a high number of indepen-

dent degrees of freedom, in which it does not respect

any manipulation done by the artist. Therefore, once

the deformation parameters are speciﬁed, it is difﬁ-

cult to control the actual resulting shape of the char-

acter in every animation frame. Furthermore, physics-

based methods are computationally expensive and

usually avoided in interactive applications. In the fu-

ture skinning research, the computational process for

obtaining believable skin motion must trade-off be-

tween these requirements: it must (1) be fast enough

to achieve interactive rate (i.e., >30 fps), (2) pro-

duce believable animation to minimize manual post-

processing time, and (3) be controllable and stable.

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