Parameterized human body model for real-time applications
Mustafa Kasap
MIRALab - University of Geneva, Switzerland
Email: mustafa.kasap@miralab.unige.ch
Abstract
We present an efficient technique for real-time human
body model generation from sizing parameters. Based on
a template model, various body sizes are generated according to the anthropometric measurement standards. These
standards are used during the design stage of the template
body model to segment it into regions where each region is
deformed by corresponding measurement parameter. Depending on the anatomical shape of the regions, deformation functions with specific control parameters are blended
to change the body sizes. Implementation of the deformation functions are based on splines and radial distance of
the surface vertices. Before applying any deformation, displacement of the vertices are filtered to preserve the continuity between neighbouring and overlapping regions. With
a set of high level parameters, this technique could be used
to generate different size models in virtual environments.
1 Introduction
There have been significant hardware and software developments in computer graphics during the last three
decades. This progression is mostly focused on achieving
realism in virtual environments. Depending on the functionality of each element in virtual environments, efficient
shading, skinning, and motion algorithms are developed to
simulate their physical properties. Most of these techniques
are focused on human body models, which are very important elements of these environments. One of the recent technique for body model generation is to use 3D body scanners [3] to acquire a virtual clone of the original model. Although this technique can capture data with resolution of
a few millimetres, post-processing the raw data takes too
much time to get a final result. In case of the games and virtual world like environments, it is not possible or efficient to
generate different size models at runtime with such devices.
For efficiency’s sake, it is clearly evident that most of the
applications sacrifice the realism. Aside from all other techniques like texture, muscle, soft tissue effects etc. which are
Nadia Magnenat-Thalmann
MIRALab - University of Geneva, Switzerland
Email: thalmann@miralab.unige.ch
used to achieve the realism, in this paper we will introduce
a new approach on different size model generation in realtime.
Since the anthropometry subject studied in computer
graphics [18] there have been many techniques developed
for realistic human body modelling from a template model.
Most of these techniques require complex computation
methods or supplementary information along with the template model. Considering real-time applications like cloth
simulation with underlying sizable body model, virtual
crowd simulation from a few number of template models,
games and virtual worlds with customizable avatars etc., it
is obvious that the efficiency of the method for body model
generation is still one of the important topics in computer
graphics.
Human body modelling field generally deals with the
underlying layers like fat tissues, muscles and skeleton to
improve the realism of the existing models during motion.
Even though recent research focuses on parametrically generating new size realistic models [33, 7] from an existing
one, they are not appropriate for real-time applications and
require preliminary processing stages.
In this paper we will demonstrate a new technique for
real time human body model generation through anthropometric measurements as parameters. In our technique,
two template models are used for male and female gender. Regarding human body anthropometry, the template
body model is divided into logical segments where each of
them corresponds to a specific body measurement landmark
defined by ISO-7250 and ISO-8559 standards [4]. Using
free form deformation methods and radial functions, desired segments of the template model are deformed independently and blended to reflect the final shape. Continuity of the sections and the normal space deformation of
the models are also considered. The proposed deformation
method also preserves the existing skinning information of
the model with simple skeleton scaling steps. In our sample implementation, 24 anthropometric measurement landmarks are considered to deform the template model. Template models are designed with 3D Studio Max [1] where it
is possible to define body segments with unique identifiers.
This is a one time process and is done manually. The proposed method can be easily integrated in the environments
that require rapid generation of virtual crowds or customizable avatars.
The remaining sections of this article is organized as follow: Section 2 examines the recent and previous works
on human body modelling field that are related with our
method. Section 3 analyses the anthropometry field and its
relation to computer graphics. In section 4, first we introduce how to design a template body model with segmentation information and then we describe the deformation
method with displacement filtering. In section 5, we introduce the implemented body deformation framework with its
sample application on specific body regions and analyze the
performance of the framework followed by conclusion.
2 Related Work
Human body deformation methodologies can be classified into creative, reconstructive, and example based approaches [33, 25]. Computationally the fastest technique,
creative approach, is the most preferable one in real-time
applications because it doesn’t require post processing, initial model database, and supplementary data. Considering that these constraints are also important for the virtual
human modelling in cyberworlds, in this section we will
mostly focus on the space deformation methods that are
subject to creative human body modelling techniques.
Space deformation is the process of mapping some of
the vertices of the underlying model from one space to the
other. Since it is a very important concept in the object modelling field, wide range of deformation techniques are developed. Earlier research on this subject starts with Barr’s [12]
work on parametric shapes called superquadrics. Scheepers used similar parametric shapes to anatomically model
the body muscles [31]. Sederberg and Parry [32] proposed
the Free Form Deformation (FFD) technique where a 3D
model embedded into a parallelepiped box, deforming the
box will also deform the underlying complex model. An
interesting application of FFD technique is presented by
Chadwick [15] who added dynamic muscle effect on top
of the skeleton with FFD technique to animate the human
body model. Coquillart [16] came with the extended version of the standard FFD method where non-parallel piped
3D lattices are used to include the irregular shapes for deformation. To have more flexibility on the deformation, Lamousin [24] logically extends the FFD by mapping it on a
non-uniform rational B-Splines(NURBS). Our approach is
based on the method named Scodef [14] which is a variant
of FFD and explained in section 4.2.
Earlier researches about anthropometric modelling of the
human body model are introduced first by Grosso et al. [18]
and then Azuola [11]. Human body model is segmented
into groups according to joints and joint deformation is
applied for animation. In Grosso’s development, a body
model is segmented into 24 polyhedral geometric primitives
with length, width and depth parameters. In contrast to our
method, model representation is not smooth and the continuity of the segments are not possible because of the distinct
polyhedral geometric primitives constructing the body.
Jianhua [23] used a new approach for body model representation. Instead of using polygonal methods, she divided
the model into slices and a radius of each slice is scaled to
achieve the muscular deformation effect. Jianhua [34] also
extended this contour based representation of body model
with metaballs [19]. Like cylindrical representation, metaballs are used for smooth and detailed modelling.
While most researchers focused on the deformation of
the model surface, a pioneering work in the anatomical
modelling field is proposed by Wilhelms [37]. Aside from
body mesh and underlying muscle, skeleton and generalized tissues are also parametrically modelled according to
the anatomical principals. Similarly Scheepers [31] developed multilayered anatomically deformable models in the
same time period. In her method, tubular shaped bi-cubic
patch meshes capped with elliptic hemispheres are attached
on both end of the corresponding skeleton. Depending on
the corresponding joint angle, underlying muscle and fat tissue structures deformed the skin surface.
Singh [35] introduced a new deformation technique
based on free form curve. She was inspired by armatures
used by sculptures. In this approach with a single wire,
direct manipulation for deformation on the model is possible. While most of the geometric techniques are inspired
from FFD, Singh [36] in contrast proposed a surface oriented FFD. She implemented a control surface defined by
a distance function around the boundary for detailed deformation.
Marinov [28] presents an efficient technique based on
multi-resolution deformation on a high resolution mesh.
Proposed deformation process handled in the GPU with the
help of pre-computed deformation operators and the gradient information. For deformation process, basically affine
transformation operators are applied on the control points.
Another approach for human body deformation is applying the sweep based method on the limbs [21]. In this approach each limb is approximated by swept based ellipsoid
which changes its size as it moves through the limb. Transition from each joint, the ellipsoid changes its orientation
through the new one. Recently, geometric muscle deformation technique was presented by Pratscher [29] who used
the multi-shell structured ellipsoids.
Cross sectional representation of the body model is used
by Zhaoqi et al. [38] to generate skeleton-driven deformations. Even this approach seems to be very similar to the
one proposed by Shen [34], Zhaoqi et al. generalizes the
cross sectional contours to preserve the original details of
the body.
3 Anthropometry
For more detailed review of the basic space deformation
techniques subject to human body modelling, the reader can
refer to Angelidis [9].
Anthropometry, that is, science of the human body measurements is directly connected with the human body modelling field. Even though anthropometric studies have been
started more then 100 years ago, one of the biggest anthropometry survey was conducted in mid-nineties by the US
Military to analyse the clothing requirements. Because of
its importance, in 1996, International Standardization Organization published anthropometric measurement standards
under the ISO-7250 reference name.
With the development of 3D human body scanners, it becomes possible to generate hundreds of accurate body measurements from a specific subject. From this point of view
CAESAR [8] project is the earliest research on anthropometric surveys conducted by 3D scanners. Dooley [17] prepared a survey report about the anthropometric modelling
programs. This report showed that the existing programs
are used by engineering companies for designing products
anthropometrically suitable for humans. Another recent
survey [27] also includes the anthropometric modelling of
more specific body parts like faces.
One of the pioneering work about parametric body
model generation from anthropometric measurements is
presented by Grosso [18] where she used cylindrical representation of the body model. Depending on the joint positions, body model segmented into parts where each part
parameterized with three main measurements; height, width
and depth. Changing each parameter according to the previously recorded anthropometric measurements will generate
different size virtual body models.
Earlier version of example based human body deformation method is presented by Seo et al. [33]. Together with
a set of scanned human body data, a template model with
appropriate skeleton attachment is designed. Regarding the
user specified model parameters, appropriate scanned data
can be found in the database. Finally a template body model
is mapped on the resulting scanned data with a skeleton
adjustment and a displacement mapping. Similarly Allen
et al. [7] developed an example based approach to transfer
a template body model on to a scanned data. They used
250 scanned data to demonstrate parameterization and reconstruction. Anguelov et al. [10] presented a pose space
deformation of the body model by using a body scanner.
Scanning the same person with different poses, a deformation space is generated. Proposed framework generates the
desired body shape according to the parameters like angles
of the joints and the eigen-coefficients of the shape.
Since the hardware accelerated computation has becomed more popular, researches shifted to GPU based deformation techniques. Rhee et al. [30] is one of the earlier
researchers who proposed real-time weighted pose space
deformation technique. From a sufficient set of example
of an articulated model they interpolated the displacements.
Regarding this information, skinning deformations are parallelized on GPU to take into account the real-time efficiency.
Considering the state of the art of human body model
deformation techniques, our impression is that most of the
studies are focused on a specific part or layer of the body
model. Among deformation methodologies, mainly the example based approaches worked on parametric model generation from a template one. Even though requesting a specific model from a database that corresponds to the desired
parameters, this approach takes long loading time and is
a bit cumbersome for real-time applications. Recent approach by Twigg [22] proposes automatic segmentation of
the body into main limbs without any markers on the body.
Even though there are other researches [7, 10] placing special markers on a scanned subject to divide the model into
segments, they are still far away from fully automating the
process. Furthermore, segmentation method mentioned in
these papers can only differentiate the model into main body
limbs like arms, legs, head etc. In fully parameterized anthropometric modelling, it is evident that the body model
has to be segmented into much more specific regions with
more definite boundaries.
4 Parametric body deformation
Initial step of the parameterized body model generation
process starts with the design of the underlying template
model. This process is the one time effort during the design
of the deformation framework. Template model segmentation process is not as simple as dividing it into the limbs.
Even though the recent skinning technique by Twigg [22]
proposes to automatically determine the main limbs, in anthropometric approach we need to determine more then 20
specific parts of the body with much more accuracy. So
this process is handled manually during the design stage
of the template model by marking the specific regions and
identifying them with unique numbers. So each identifier
corresponds to a set of vertices, or, more precisely, specific region of the main body mesh. Determination of the
regions is a kind of heuristic process based on the major
measurement landmarks extracted from ISO-7250 and ISO8559 standards that are illustrated in figure 1. We categorize
these regions into three groups:
• Regions where the deformation is applied vertically,
more specifically height measurements, i.e. waist
height, inside leg length.
• Regions where the girth deformation is applied on
joints, i.e. knee girth, wrist girth.
• Regions where the girth deformation is applied inbetween two joints or regions, i.e. thigh girth, calf
girth, waist girth.
Deformation of each region is achieved by modifying the
value of corresponding measurement parameter affecting at
least two deformation functions. Also two different blending operations are considered during the deformation. First,
the result of corresponding deformations for each region are
blended and stored in the related deformation bins. Each
body region has its own deformation bin which holds the final displacement values that will be applied on the template
model. Second, displacement values of all overlapping regions are blended by applying another smoothing function
described in section 4.3. This second step is the mandatory
process to preserve the continuity between the overlapping
regions. Final results are reflected on the template model to
represent the desired size model. In interactive real-time applications a user can change one parameter at a time, so the
mentioned steps are applied on the region that corresponds
to the modified parameter and the overlapping regions. This
will reduce the calculation overload by dealing with a subset of thousands of vertices on the mesh.
4.1
Model segmentation
Deforming a body model according to an anthropometric parameter requires the exact specification of the region
that will be deformed. Preserving the smoothness and continuity of the neighbouring or overlapping regions strictly
depends on this specification. Existing automatic body segmentation methods can determine only the main limbs of
the body model and require additional information. In our
case, a segmented template body model is manually generated only once in the model design stage and doesn’t require
any modifications afterwards.
Figure 1 represents some of the measurement landmarks
that are used for deformation on the template body model.
To achieve deformation around a specific landmark, corresponding group of indices around the landmark are manually defined. Indices groups are defined with 3D Studio
Max [1] which is also used to design the template body
model. Selecting a subgroup of the vertices on the main
mesh, it is possible to assign a unique identifier for the selected vertices. The exported model file already contains
this information making it possible to determine the identifier of each vertex. Figure 2 illustrates some of the regions
corresponding to the landmarks represented in figure 1.
Figure 1. Some of the major body measurement landmarks.
Figure 2. Some of the regions defined on the
template model.
Vertices in each region are defined as segment M . In a T
posture, regions on the arm are oriented on horizontal direction and the other regions in a vertical direction. According to the orientation of each segment depending on
the model’s posture, number and type of the deformation
functions are manually defined. In case of Bézier based deformation functions, control points are preliminary adjusted
to give the bumpy effect along with the muscles and the fat
tissue.
4.2
Regional Deformation
We extended the deformation method called Scodef [14]
by using a constraint region instead of a constraint point
and used a different type and variable number deformation
functions for each region.
Let M be the segment of a mesh that is defined in section 4.1. A set of distinct nodes in the segment is given
by
V = {vi }ni=0 ⊂ R3
(1)
where n is the number of vertexes in the segment. We use
notation
vi = (xi , yi , zi )
vi,1 = xi , vi,2 = yi , vi,3 = zi
(2)
for i th point vi ∈ V . Let d be the deformation function
where d : R3 → R3 and
d(vi ) = di = vi +
k
f (L(v)) = (a, b, c)
sj ni fj (L(vi )).
(3)
j=1
Here k is the number of deformation functions that will
be applied on the M and di is the new position of vi after deformation. ni is the normal vector of vi which is not
changed after deformation because the deformation is in the
same direction as the normal. sj is the scale factor of the j th
deformation function where s = (sx , sy , sz ), 1 ≤ si , i =
1, 2, 3 and each component separately defines the scale factor on the corresponding dimension. fj is representing
the j th deformation function where f : R3 → R3 , 0 ≤
f (v)i ≤ 1, i = 1, 2, 3 and L is normalized local coordinate
function where L : R3 → R3 , 0 ≤ L(v)i ≤ 1, i = 1, 2, 3.
Calculation of the normalized local coordinate function L
is based on the bounding box X of M . Figure 3 represents
the process in one of the dimensions and L is defined as
L(vi ) =
First type of deformation function is defined as
vi,j − Xmin,j
vi,2 − Xmin,2
,
,
Xmax,j − Xmin,j Xmax,2 − Xmin,2
vi,3 − Xmin,3
Xmax,3 − Xmin,3
where two components of the resulting triplet are equal to
0 and the third component t is equal to C(L(v)t ). t is determined according to the orientation of the segment M and
the axis where the required deformation will take place.
For the second type of deformation function we used angular distance of the vertex. Figure 4a is a horizontal plane
which shows this process over the left leg.
Figure 4. a) Angular distance. b) Nonlinear
deformation.
(4)
By using equation 4 we find the local coordinate of the vertex on desired dimension. Taking the center of the bounding
box as origin, we determine how far the vertex from the origin as in Figure 4a. This normalized (angular) distance is
used as second deformation function’s contribution to the
equation 3. This deformation can be constrained to a single
side, namely front or back part of the origin as in Figure 4b.
Therefore a half elliptic rather then a circular growth can be
achieved. This process is represented in figure 4b where the
back muscle of the leg is much more deformed compared to
the front leg.
4.3
Figure 3. Local coordinate on bounding box.
As from the number k, two different types of deformation
functions are used per segment M . For the first type of
deformation function we use Bézier curve defined in equation 5.
Bi,n =
C(u) =
n
n!
ui (1 − u)n−i
i!(n − i)!
Bi,n (u)Pi , 0 ≤ u ≤ 1
(6)
(5)
i=0
where C(u) is the n th degree Bézier curve, Bi,n is the n th
degree Bernstein polynomials [13, 26].
Smoothing
Deforming a segment without any filtering stage will result in discontinuous passes at the boundaries of the regions.
To demonstrate this situation, only the calf part of the model
is deformed without any smoothing operator applied. The
calf region of the model is clearly illustrated in figure 2. The
result of the deformation operator that is described in section 4.2 is represented in figure 5a with and without smoothing operator applied.
To prevent such irregularities, we perform filtering over the
final displacement vectors. This stage is necessary in two
cases, firstly the default case; the boundary of the segment
is neighbours another segment, secondly; two or more segments overlap. In both cases the boundary vertexes must
be deformed smoothly. These two cases are represented in
figure 6 where the red (calf) and green (knee) segments are
for small segments like ankle, knee etc. Figure 5 demonstrates the deformed body part, calf, with and without filtering function.
4.4
Curvature of the deformation functions and an example application
Figure 5. Discrete and Smooth pass a, b.
neighbours, the red and blue (ankle) segments are partially
overlapping.
Figure 6. Calf, Ankle, Knee body segments.
On each segment, we applied the Tukey window [20] as the
smoothing function with different parameters. The Tukey
window, also called cosine-tapered window, smoothly sets
the displacement value on the boundaries of the segment to
zero. By changing the parameter α from 0 to 1, it evolves
from a rectangle to the Hanning window [20]. Tukey window plot with different α values is represented on figure 7.
1
← α=0
0.8
← α=0.25
0.6
← α=0.5
0.4
← α=0.75
0.2
← α=1
In section 4.2 deformation functions and their applications
on a region are explained. First the type of the deformation
function is Bézier which needs the specification of the control points values. The segmented body parts have mainly
the similar structure, so in this section we give two different
segments as an example for the deformation. The first segment is the calf part of the body and it looks like a cylinder
like the other limbs but thanks to the muscles at the back
of the calf, its shape behaves like a smooth curved surface.
The second segment is the belly and because of the fat tissue around the belly, its shape looks like a curved surface
too. Curvature of these surfaces are represented with Bézier
functions where the control points are adjusted in a heuristic
way.
Figure 8. Deformation functions on horizontal and vertical planes.
0
0
0.2
0.4
0.6
0.8
1
Figure 7. Tukey smoothing window.
The window is defined by
w(u)=
1.0,
0.5 [1.0+cos(A)]
0≤|u|<α N
2
αN
2 ≤|u|≤N
,
(7)
where
u−α N
A = π 2(1−α)2N , u = L(v)t ∗ N.
2
and N is the number of samples uniformly spaced on x axis,
w(u) is the corresponding value on y axis and L(v) is obtained from equation 6. Here α determines the ratio of taper
on constant sections. Small α values are used for smoothing
large segments like thigh, calf etc., large α values are used
The calf part of the body and the corresponding deformation functions’ plot are demonstrated in the first example of Figure 8. Angular deformation presented in Figure 4
is applied on the horizontal plane and Bézier based deformation functions are used on the vertical plane. Curvature
of these Bézier functions decreases at the end points of the
back half part of the calf turning into a straight line. On
the front part, linear deformation is applied since there are
no muscles. In addition to these deformations, a smoothing
function explained in section 4.3 is applied on the segment
M both horizontally and vertically to preserve continuity
between regions. The second example in Figure 8 represents the deformation of the belly part. Again two different
deformation functions are applied to generate the belly effect. Here again the smoothing functions in both direction
applied to achieve continuity at the boundary of segment
M.
5 Implementation
We implemented a framework where the above mentioned techniques are applied on a template human body
model generated by designers. Segmentation information
is imported to the framework by using a common 3D data
exchange format called COLLADA [2]. We have modified
the open source 3DStudioMax to Collada exporter plug-in
to export the segmentation (smoothing group) information.
The Collada file of template body model also includes the
skinning information for animation which is defined in the
design stage. Apart from the deformations, the geometry
of the model is not changed so the skinning information is
not effected and still usable by the animation engine. For
the length measurements, only which change the size of the
body in horizontal and vertical directions, we use ordinary
scaling operators to resize the corresponding bones.
Our framework is based on OpenSceneGraph [6]; it is
a high performance 3D graphic toolkit entirely written in
Standard C++ and OpenGL. It was tested with 2.4GHz Pentium4 PC with an nVIDIA QuadroFX 1400 graphic card
and 1GB system memory running on Microsoft Windows
XP. In this framework we bypass the standard rendering
pipeline of OpenGL by using GPU based shaders for skin
rendering with light effects. We have tested our framework
by using 3D human body model consisting of 116K vertices
with 1024 × 1024 skin texture. Off-screen deformation
time of specific body part takes then less than 0.5 second
to render. Results for real-time deformation tests with and
without per pixel lightining effect can be found in table 1.
It is important to note that virtual crowd optimizations are
not applied on the scene.
#Model / Effect
1
2
3
4
5
NA
73
65
60
57
53
Light
44
34
25
24
23
Table 1. Deformation performance in fps.
6 Conclusions
Study of fast deformation techniques for generating human body models with different measurement ranges is one
of the main problems of computer graphics. Over the recent years, many of attempts have been performed on this
topic to achieve visually perfect models. In this work we
present a fast and computationally cost effective schema for
real-time human body model deformation. Anthropometric measurement parameters are used to deform the tem-
plate model which makes it possible to generate standardized range of body sizes. As a future work, segmentation of
the template body model and determination of the value of
the control points should be done automatically. This will
allow any designed or scanned model to be rapidly sized to
generate variations. Changing the skin and face texture of
the new size models at runtime is another addition for realism which we used in crowd simulation but not explained
in this paper.
Acknowledgements
The authors would like to thank to Nedjma Cadi for her
contribution in designing template body models. This work
is supported partly by the EU project LEAPFROG [5].
Figure 9. Template model and deformed
clones.
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