3D Realistic Animation of a Tennis Player
Mustafa Kasap1 , Matthias Holländer2 , Anil Aksay3 , Philip Kelly4 , David
Monaghan4 , Ciarán Ó Conaire4 , Nadia Magnenat-Thalmann1 , Tamy
Boubekeur2 , Ebroul Izquierdo3 and Noel E. O’Connor4
1
MIRALab, Universite de Geneve (UNIGE), Switzerland
2
Computer graphics group, Telecom Paristech, France
3
Multimedia and Vision Group (MMV), Queen Mary University London, UK
4
CLARITY: Centre for Sensor Web Technologies, Dublin City University, Ireland
[email protected],[email protected],anil.aksay@elec.
qmul.ac.uk,[email protected]
Abstract. In this paper we present the progress of collaborative ongoing
work into the realistic 3D animation of a tennis player from the 3DLife
project. The main focus of this work revolves around producing a realistic
3D virtual avatar of an athlete, capturing realistic motion data of the
athlete via a pre-recorded motion database, streaming the motion over
a network and displaying the virtual human model of the athlete in
a virtual and fully interactive environment. While we have focused on
tennis in this paper we believe our methods can be generalised to a wide
range of other activities both in sports and otherwise.
Keywords: Body Scanning, Scalable Rendering, Motion Capture, Inertial Measurement Units, Immersive Environment
1
Introduction
3DLife [1] is a European Union funded project that aims to integrate research
conducted within Europe in the field of Media Internet. The next generation
of Media Computing services will become the cornerstone of the Information
Society, having a significant impact not only on the entertainment industry, but
also in the way that society delivers key services such as health care, learning
and commerce to all, irrespective of geographical location or access device. As
such, 3Dlife is devoted to developing and integrating enabling technologies that
could make interaction between humans in virtual on-line environments easier,
more reliable and more realistic. In order to obtain this goal, the integration
of recent progress in 3D data acquisition and processing, autonomous avatars,
real-time rendering, interaction in virtual worlds and networking is required. In
this work, we present a framework, built in the first 6 months of the 3DLife
network of excellence, that integrates some of enabling technologies required for
this strategic vision.
In this paper, we integrate a number of technologies from 3DLife consortium
members, resulting in system framework that can simulate the realistic movements of a real-world human body in an on-line virtual environment. As such,
2
Kasap, et al.
the real-world motion of a human body is captured, and rendered as a realistic
avatar in an immersive virtual environment. As a real case scenario, simulation
of a tennis player in motion will be demonstrated. Several advanced tools and
computer graphics methods are used to achieve the visual realism. The production pipeline of this work consists of the following stages: Firstly a human model
is scanned and post-processed to generate high quality mesh with texture. Secondly these models are processed for scalable rendering. Thirdly the resulting
high quality mesh is mapped to an animation library and finally the resulting
model is streamed through a network and rendered in an interactive environment
for a visual demonstration. By simulating a realistic virtual animation of a tennis player and displaying this in the context of a completing interactive virtual
environment we believe that we will provide a level of realism and immersion
not previously experienced.
Section 2 outlines our proposed system framework. In Section 3 an overview
of the digital creation of the realistic avatars is given including surface reconstruction and character skinning. In Section 4 a description of the rending process
is presented. The procedure for acquiring accurate and realistic motion animation data is explained in Section 5 and networking is outlined in Section 6. An
example screen shot of the system in presented in 7 and the conclusion is given
in Section 8.
2
System Overview
Figure 1 illustrates an overview of the different stages of the proposed system
framework. The first stage involves generating a realistic virtual human body
model for the real-world tennis player. In this work, this is achieved using a full
body human laser scanner to obtain highly detailed 3D scans of the human subject. These scans are then post-processed in order to map a skeletal structure,
skin, facial textures and virtual clothing to the human model as detailed in Section 3. In this work, as the system incorporates a scalable rendering framework
(see Section 4), the model is also processed for scalable and real-time rendering.
The second phase of the proposed system framework allows realistic movements from real-world human subjects to be conferred to virtual human models.
Motion is incorporated into the model animation by employing human motion
reconstruction techniques that employ a number of wireless wearable accelerometers and a motion database. This motion, when transferred to the skeletal structure in the model, provides realistic human-like movements of the virtual human
body model (see stage three in Figure 1).
The fourth stage of the system framework securely transfers the acquired
motion data over a network. This data can then be streamed to a client and
loaded onto a previously downloaded model, thus allowing for real-time motion
to be transmitted and reconstructed via the virtual human model at a different
location. The client consists of an interactive rendering environment, such a
virtual tennis court, that allows either the streaming of pre-recorded animation
3D Realistic Animation of a Tennis Player
3
Fig. 1. Overall system outline.
files or real-time animation motion data via a network connection – see the final
stage as depicted in Figure 1.
3
Human body models
3D human body models are core elements of computer graphics environments
such as games, virtual modelling tools and virtual reality applications. Recent
technological advances in computer graphic techniques and hardware development have made it possible to use more realistic models in these virtual environments, including but not restricted to muscle and fat tissue deformation effects,
during animation.
The primary characters in the majority of these virtual environments are
human body models and they require specific techniques for real-time animation
and realism. Humans spent massive amounts of time and brain-power visually
inspecting facial and hand movements, primarily for communication, and this
has made us very astute at detecting artificial humans. Due to the importance
of these factors specialized research areas focused on face, hand, skin, muscle
modelling and skeleton attachment to generate realistic models that will improve
the quality and realism of the environments. Human body models which are
subject to all those techniques and environments are either designed by 3D
model designers or acquired by 3D body scanners like the ones from Human
Solutions [19].
3.1
Surface Reconstruction
Raw data generated from a full body human laser scanner can consist of a
million point coordinates captured from subject’s surface. The resulting raw data
4
Kasap, et al.
Fig. 2. Scanner generated point cloud to 3D textured model.
requires post-processing to generate a mesh from the point cloud, see Figure 2 [9].
This post-processing operation consists of several minor stages such as outlier
removal, noise reduction, simplification, normals estimation and triangulation
[14]. In addition to those stages the final mesh is textured with the images of
the scanned subject that are captured from different multiple angles.
In almost all the post-processing stages the k-Nearest Neighbour (k-NN) of
each point in the input data set is used. To remove the outliers vertices above the
average distance of the k-NN are eliminated. To reduce the noise the following
operation is applied on each vertex of the input set. A plane is fitted on the k-NN
of the vertex under the operation. Then the vertex is projected on this plane
to have a smoother trajectory along the k-NNs. Similarly the normal direction
of each vertex in the input-set is estimated by assigning the same fitted plane’s
normal vector to the vertex under the operation. To reconstruct a surface from
the captured points, post-processed input point sets with estimated normals are
used to compute an implicit function. Finally the iso-surface of this function is
extracted to reconstruct the scanned surface [17]. In addition to these stages the
reconstructed model surface is also textured to increase the visual realism. For
the texture mapping several images of the model are captured from different
view angles. Subsequently these images are mapped on the 3D model by using
a ray-casting approach together with colour camera coordinates, see Figure 3.
3.2
Character Skinning
Visually realistic character simulation is dependent not only on the realistic 3D
model but also requires the model to be animated with life-like motion. Adding
motion to a 3D static model requires additional information about the models
skeleton and skin structure, see Figure 4. This information is generated by attaching a virtual skeleton onto the model and skinning it. Skinning is the process
to assign deformation weights to model vertices [15]. These weights define the
deformation relation between the vertex and the skeleton. When the underlying
3D Realistic Animation of a Tennis Player
5
Fig. 3. Rays are casted from colour cameras to construct texture map.
virtual skeleton moves, corresponding vertices follow the skeleton with the ratio of the pre-defined skin weight. To animate the skeleton, mainly the motion
capture devices are used.
4
Scalable rendering
Scalable rendering means adapting the rendering process on the fly to the capabilities of the client’s machine. In many cases, this means using a high resolution
mesh for high-end computers and low resolution meshes for low-cost computers.
Usually this involves the process of up-sampling a low resolution mesh for highend computers or down-sampling a high resolution mesh for low-end machines.
Today’s graphics processors are capable of performing the former one on-the-fly
by means of real time tessellation. As a consequence, animation methods (e.g.
skinning) can be applied on the low resolution mesh before up sampling this
mesh to a higher resolution for high quality display. This reduces drastically
all non rendering-related computations (e.g. animation, physics interaction) in
contrast to slow animation on a high resolution mesh. Moreover, it lowers the
memory footprint as only a low resolution mesh has to be kept in memory and
minimizes the data-transfer between the main memory and the graphics card.
In this work, a specific piece of software is being designed to support this
stage of the system framework. The program is able to load the virtual human
model person, as obtained from Section 3, and display it on a wide range of
graphics hardware implementations. To achieve this aim, we maintain a simpli-
6
Kasap, et al.
Fig. 4. Character Skinning. From left-to-right; (a) Static model; (b) Virtual skeleton
attached; (c) Skinned (left thigh weights visualized); (d-e) Animated.
fied version of the scan in main memory and use the ability of modern graphics
cards to process and up-sample geometry fast and efficiently, thus improving the
geometric and visual quality of real-time rendering. Real-time tessellation methods offer the ability to up-sample 3D surface meshes on-the-fly during rendering.
This up-sampling relies on 3 major components: (a) a tessellation kernel, which
can be implemented on the GPU (Graphics processing unit) or is available as a
hardware unit; (b) the surface model, which defines the positions of the newly
inserted vertices – we focus on recent visually smooth models; and (c) the adaptability, which tailors the spatially varying distribution of newly inserted samples
on the input surface. In this work we focus on the last component and introduce a new view-dependent adaptability metric that builds upon both intrinsic
and extrinsic criteria. As a result, we obtain better vertex distributions around
important features in the tessellated mesh.
In this work, we start by simplifying the raw input body scan mesh using
a mesh decimation approach. We recursively use an edge collapse operator [8]
using a combination of the seminal Quadric Error Metric [7], together with a
normal-based metric derived from the variational shape approximation technique
[6], which is more suitable for preserving sharp features. A single iteration of
the pre-processing stage is performed and the simplified mesh can be set to a
resolution which is suitable for the target platform (in the order of 3,000 triangles
for a standard GPU).
At run time, we use the cheap, visually smooth, Phong Tessellation operator
[2], combined with an adaptive tessellation kernel based on refinement patterns
and hardware instantiation [3,4] to perform the aforementioned up-sampling of
a mesh. The latter procedure can be replaced seamlessly using recent hardware
tessellation units. The tessellation process, as shown in Figure 5, is entirely
performed on the GPU and has almost no prerequisites. Furthermore, it can
3D Realistic Animation of a Tennis Player
7
Fig. 5. From a simplified scanned model, real-time tessellation.
reach high frame-rates, while adapting the level of detail of the character to
their screen-space occupancy – as such it can therefore balance the rendering
workload dynamically. Note that right before up-sampling, we apply skinning
on all vertices of the coarse mesh. This process can be implemented on the GPU
but at such a low resolution the CPU is not a bottleneck here.
5
Motion data
In order to realistically simulate an activity in a virtual environment, the 3D
movements of each person participating in the activity must be faithfully and
efficiently captured. With accurate 3D motion information, each character in
the environment can be animated in a realistic manner, resulting in not only
naturalistic character movement but also increasing the ability for characters to
efficiently interact with the environment surrounding them.
The traditional gold standard technique for motion capture is to use an expensive multi-camera optical system, such as the Vicon [21] or Coda [5] systems.
In either system, markers (which may be active [21] or passive [5]) are fixed on
the human body in a number of predefined locations. These markers are tracked
in 3D by a number of pre-calibrated cameras. At any given time instant, if the
positional information of each marker location on the human body is known,
8
Kasap, et al.
a pre-defined 3D human skeleton can be fit to the measured marker positions.
Over a time period, these individual body poses are combined to obtain an animation of the captured 3D movement of the human character. The advantages of
such systems are their high degree of accuracy and frame-rate, allowing precise
and rapid human motion to be captured. As such, these techniques are regularly
employed by sports science professionals to analyse movement and maximise
athlete training and performance; animators and video game designers to make
their animations more realistic; and the film industry for naturalistic motion in
special effects.
Although accurate, optical motion capture systems are cumbersome for capturing some activities – especially sporting scenarios. Motion capture rigs are
typically expensive, difficult to set-up, and confine motion to a pre-calibrated
area that tends to be small unless a large number of cameras are available – a
difficulty for athletes in particular, who often move very quickly through large
volumes of space in a match. In order to increase the size of the capture area
to even a relatively small sporting field of play, such as a tennis court, a very
expensive installation would be required – an unobtainable scenario for amateur
athletes or home enthusiasts. In addition, and perhaps most importantly for realtime human-computer interaction in a virtual environment, few motion capture
systems provide real-time motion of a character’s skeleton – rather, once the
data is captured, it requires a semi-automatic clean-up process requiring human
interaction and intervention to process and manipulate the captured data. This
clean-up process ensures that the reconstructed motion is correct at times when
the tacking of markers may be lost due to rapid movement or occlusion.
In this work, we would like to make the system relatively cheap, near real-time
and unobtrusive to wear. In order to obtain viable biomechanical information
on player movement, the reconstructed motion should also be as accurate as
possible when compared to the real movements of the player. For sports with
a high level of motion, such as tennis, golf and baseball, the most important
motions to be captured are usually a high velocity motion that takes place in
a very small time interval, as such the proposed technique should have a high
frame-rate in order to accurately capture these rapid motions. Recent work into
the use of cheap inertial sensors to extract real-time body motion data provide
an insight into possible techniques for achieving this aim. In our approach the
motion from an actor (or athlete) is captured using a small number of ±10G
tri-axial accelerometers placed on the human body. These accelerometers are
light weight, relatively cheap, easy to set-up and unobtrusive for the athlete to
wear for the vast majority of motions. Data from each accelerometer is sampled
at 100Hz, time-stamped and transmitted via a serial wireless link to a data
collecting base station. In our experiments, 1 accelerometer was placed onto
each forearm and shin, 1 was positioned on the chest, and 1 was located on
the lower back. These positions were strategically chosen to obtain input data
from each limb, root position and torso. However, the locations used depends on
where on the body accurate motion reconstruction is required. In cases where no
3D Realistic Animation of a Tennis Player
9
inertial measurement units are used on limbs, the system will recreate plausible
movements based solely on the accelerometer input from other areas of the body.
Inertial wireless accelerometers are small and lightweight enough so as not to
interfere with a sports athlete’s performance when worn in clothing. An example
of this technology can be seen embedded in the Nike+iPod Sports Kit [16], a
device which measures and records the distance and pace of a walk or run. The
technology consists of a small wireless accelerometer attached to or embedded
in a shoe that detects when a foot is in contact with the ground, this information is then transmitted and combined with timing information to determine
such statistics as the elapsed time of the workout, the distance travelled, pace,
or calories burned by the individual wearing the shoes. Although not accurate
enough for sport scientists (accurate to plus or minus 5%[13]) it is perceived
to be accurate enough for the needs of an average user. The use of accelerometers in this sports kit is solely as a data-gathering technique to supply data for
personal metrics on human performance in sports. It does not supply real time
interaction between the user and a virtual environment, or reconstruct the pose
of the actor using it. The use of accelerometers in the Nintendo Wii Remote
[24] can supply this information at a coarse degree of accuracy. Gaming with
the Wii remote can be regarded as a performance animation experience [18] –
the accelerometers allow a player to interact with virtual objects and perform
coarse movements to perform actions required in the game. Similar applications
using in-built accelerometers in smart phones (such as the iPhone) to control
movement in games have also been created. These technologies however only
acquire movement/position information for a small part of the body.
Techniques for full body human motion capture have also been developed.
When held in a static position, a tri-axial accelerometer outputs a gravity vector that points towards the earth. This alone is enough to determine the sensor’s pitch and roll. Techniques, such as [12,20], use this information plus the
assumption of low acceleration motion to obtain temporal poses from a previously determined body position and the double integration of accelerations to
obtain movement between consecutive time segments. XSens [25] extends this
approach to include the use of both accelerometers and gyroscopes in a single
inertial measurement unit (IMU). As such, these techniques require no external
cameras, emitters or markers are needed for relative motions, in addition the approaches have a large degree of portability and are suitable for capture in large
capture areas. However, they are prone to positional drift which can compound
over time and do not handle high velocity motion sequences well. To prevent this
drift acoustic-inertial trackers have also proposed [22], which uses an acoustic
subsystem to measure distances between markers, however the suit itself could
be cumbersome for some activities such as high energy sports.
In this work, we adopt the approach of [18] to reconstruct human motion
using a number of wireless accelerometers. In addition, we extend their work to
also reconstruct the motion of the full body (and not just the top half). We also
improve the approach of [18] at transition points. Blending at transitions (i.e.
between two or more distinct movements in the database), while making the
10
Kasap, et al.
animation look smooth and naturalistic, may mean that artefacts that did not
exist in the original motions can be introduced into the animation. Their proposed system escapes the problem of temporal drift by avoiding the integration
of accelerometers, instead it determines the motion of a character by comparing
the input accelerometer values against those defined in a pre-captured motion
database. The section of the database that has the closest accelerometer readings
to the input from the real-world character is chosen to represent the movement of
the real-world character. The approach adopted can be broken down into three
main stages;
(1) before capturing an athlete, a database of motion data is built up in
the lab, this database consists of motions that are specific to the real-world
movements they expect to capture at run-time (in their work, this consisted of a
set of upper body actions including stretching, jumping jacks, and mock tennis
swings);
(2) a calibration stage, whereby the accelerometers in the database are aligned
with those of the real-world player; and
(3) a search stage, whereby segments from the motion capture database that
consist of similar accelerations to those performed by the real world player are
obtained and provided as an output to the system.
In order to perform smooth motion, blending of motion or “jumps” between
locations in the database are performed. In the work of [18] the motion of the
upper half of the character was obtained – the motion of the root and legs
were not reconstructed. A common unwanted issue is known as footskate [10], a
distracting error is when the character’s feet move when they ought to remain
planted to the ground. As such, we remove footskate using a similar technique
to the inverse kinematics based approach proposed by [11].
6
Networking Implementation
In order to accommodate users with varying hardware and networking capabilities, virtual human models are converted into several resolutions. In the beginning of the session, the client downloads a player avatar with the appropriate
resolutions, similar to SDP (Session Description Protocol), according to user
preference or network bandwidth. Virtual human models are stored in COLLADA file format and the data size is between 2 to 30 MBytes. In order to ensure error-free data transmission, virtual human models are transmitted before
the session starts using TCP/IP protocol. In order to accommodate real-time
animation, motion data is transmitted using UDP protocol with basic error detection capabilities such as in-packet CRC (Cyclic redundancy checks). After
the validation of the packets, animation motion data is sent to the animation
module for rendering.
3D Realistic Animation of a Tennis Player
7
11
Working Prototype
In this work, three human avatars were created, using over 50 body scans in
the process. For the motion capture module, a pre-captured database of sportsspecific motion data must be obtained. This database can be captured in short
segments in the lab, but the preferred approach is in a sports arena during
a competitive match scenario. When a player is asked to move around a lab
providing sample shots, the player is performing without a specific goal, whereas
there are multiple goals in a competitive scenario: the player must play the most
tactically relevant shot, get the ball over the net, keep it in play, and move around
the court to get into position for the next shot. We captured 5 minutes and 12
seconds of data of a high performance tennis athlete on court at 120Hz using a
12 camera Vicon motion capture system. During the capture session, the player
was asked to play a variety of tennis shots – serves, forehands and backhands
– into specific areas of the opposing player’s court who fed balls for non-serve
shots. He was asked to perform typical tennis movements around the capture
area, hitting shots at specific points to provide us with realistic player motion
dynamics in a competitive scenario. However, the entire of the court could not
be covered by the motion capture system. Rather, a smaller area around the
court baseline was used, corresponding to the region where players spend most
of their time, and the player was asked to stay within these boundaries. At the
end of the capture session, two players participated in a competitive match to
win individual points using standard tennis rules. An evaluation of the proposed
technique revealed an average accuracy of between 10−20 degrees for mean joint
angle error over 16 tennis motion sequences consisting of 6 different types of
tennis movement (Serve, Forehand, Backhand, Motion, Move and Hit, Standard
Games). For all these evaluations, a remove-and-test evaluation approach was
adopted. In general, if a motion was performed in a similar manner to a motion
in a database, good reconstruction was obtained. However, poor reconstruction
resulted when a player performed significantly novel motions.
Figure 6 shows the virtual tennis environment created in this work with
realistically animated human models simulated and accurate, life-like, motion
inferred on the models. The 3D movements of each human-model in the environment can viewed from any angle and replayed or slowed down by the user.
In this figure, the rendered tennis players are located in a virtual tennis court
environment obtained from Google 3D Warehouse [23].
8
Conclusions and Future Work
In this paper, we presented the progress of collaborative ongoing work into the
realistic 3D animation of a tennis player from the 3D-Life project. The main
focus of this work revolves around producing a realistic 3D virtual avatar of an
athlete, capturing realistic motion data of the athlete via a pre-recorded motion
database, streaming the motion over a network and displaying the virtual human
model of the athlete in a virtual and fully interactive environment. With accurate
12
Kasap, et al.
Fig. 6. A working prototype of the virtual tennis environment with animated human
models.
3D motion information, simulated via motion capture and motion database, each
character in the environment can be animated in a realistic manner, resulting
in not only naturalistic character movement but also increasing the ability for
characters to efficiently interact with the environment surrounding them. While
we have focused on tennis in this paper we believe our methods can be generalised
to a wide range of other activities both in sports and otherwise. Future work
includes the synchronizing of audio data transmission over network, efficiently
tracking the tennis ball and integrating this data into the virtual environment,
full real-time tennis game simulation and increasing the level of detail in the
simulations.
Acknowledgements
This research was partially supported by the European Commission under contract FP7-247688 3DLife. This work is supported by Science Foundation Ireland
under grant 07/CE/I1147.
References
1. 3DLife: http://www.3dlife-noe.eu (2010)
2. Boubekeur, T., Alexa, M.: Phong tessellation. In: ACM Transactions on Graphics
(2008)
3D Realistic Animation of a Tennis Player
13
3. Boubekeur, T., Schlick, C.: Generic mesh refinement on gpu. In: SIGGRAPH/EUROGRAPHICS Conference On Graphics Hardware. pp. 99–104
(2005)
4. Boubekeur, T., Schlick, C.: A flexible kernel for adaptive mesh refinement on gpu.
In: Computer Graphics Forum. pp. 102–113 (2007)
5. Coda: http://www.codamotion.com (2010)
6. David Cohen-Steiner, P.A., Desbrun, M.: Variational shape approximation. In:
ACM Transactions on Graphics. pp. 905–914 (2004)
7. Garland, M., Heckbert, P.S.: Simplifying surfaces with color and texture using
quadric error metrics. In: Siggraph. pp. 263–269 (1998)
8. Hoppe, H.: Progressive meshes. In: SIGGRAPH / ACM Special Interest Group on
Computer Graphics and Interactive Techniques. pp. 99–108 (1996)
9. Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., Stuetzle, W.: Surface reconstruction from unorganized points. In: SIGGRAPH / ACM Special Interest Group
on Computer Graphics and Interactive Techniques. pp. 71–77 (1992)
10. Ikemoto, L., Arikan, O., Forsyth, D.: Knowing when to put your foot down. In: I3D
’06: Proceedings of the 2006 symposium on Interactive 3D graphics and games. pp.
49–53. ACM, New York, NY, USA (2006)
11. Kovar, L., Schreiner, J., Gleicher, M.: Footskate cleanup for motion capture editing.
In: SCA ’02: Proceedings of the 2002 ACM SIGGRAPH/Eurographics symposium
on Computer animation. pp. 97–104. ACM, New York, NY, USA (2002)
12. Lee, J., Ha, I.: Real-time motion capture for a human body using accelerometers.
Robotica 19(6), 601–610 (2001)
13. McClusky, M.: The nike experiment: How the shoe giant unleashed the power of
personal metrics. Med-Tech: Health (Wired) 17(7) (2009)
14. Michael Kazhdan, M.B., Hoppe, H.: Poisson surface reconstruction. In: SIGGRAPH / ACM Symp. on Geometry Processing. pp. 61–70 (2006)
15. N. Magnenat-Thalmann, R. Laperrire, D.T.: Joint-dependent local deformations
for hand animation and object grasping. In: Canadian Information Processing Society. pp. 26–33 (1989)
16. Nike+iPod: http://www.apple.com/ipod/nike/ (2010)
17. Rineau, L., Yvinec, M.: A generic software design for delaunay refinement meshing.
In: Elsevier Science Publishers B. V. pp. 100–110 (2007)
18. Slyper, R., Hodgins, J.: Action capture with accelerometers. In: 2008 ACM SIGGRAPH / Eurographics Symposium on Computer Animation (Jul 2008)
19. Solutions, H.: http://www.human-solutions.com (2010)
20. Tiesel, J., Loviscach, J.: A mobile low-cost motion capture system based on accelerometers. pp. II: 437–446 (2006)
21. Vicon: http://www.vicon.com (2010)
22. Vlasic, D., Adelsberger, R., Vannucci, G., Barnwell, J., Gross, M., Matusik, W.,
Popović, J.: Practical motion capture in everyday surroundings. ACM Trans.
Graph. 26(3), 35 (2007)
23. Warehouse, G.S.D.M.: http://sketchup.google.com/3dwarehouse/ (2010)
24. Wii, N.: http://www.nintendo.com/wii (2010)
25. XSens: http://www.xsens.com (2010)