1. Motion Capture: History, Approaches, Applications, and Future trends
By: Ryan Frost
MSIM 742 : Visualization II
Old Dominion University

2. Introduction
Motion capture is the recording of human body movement (or other movement) for immediate or delayed analysis and playback
Motion capture refers to the technique of recording the actions of human actors, and using that information to animate digital character models in 3D animation

3. History Interest in studying motion dates back to 1400’s
1400’s: Leonardo da Vinci
1500’s: Galileo
1600’s: Borelli
Newton, Bernoulli, Euler, Pioseuille, and Young
1900’s:Muybridge, Marey
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In the late 1400’s Leonardo da Vinci studied and sought to describe the mechanics of standing, walking, sitting and jumping.
In the 1500’s Galileo attempted to analyze human biomechanical functions through mathematical functions and expressions.
Then in the 1600’s Borelli was able to identify the forces required for equilibrium in various joints of the human body.
These early pioneers made great strides in the analysis of motion, and human biomechanics, and there were a number of equally famous scientists who followed in the next couple of decades who continued to better understand motion and its complex mechanics.
In the late 1800’s and early 1900’s Eadweard Muybridge was the first to utilize motion picture capture to better understand, analyze and visualize motion. Muybridge developed a photographic processing and electrical trigger apparatus that allowed for rapid fire motion capture. The famous series of pictures above, ‘The Horse in Motion’ was used to settle a long standing debate as to whether all four hooves of a horse left the ground during galloping.
*An interesting note is that previous scientist had theorized that the legs may all leave the ground when the legs are fully extended forward and back. With the use of the photographic series, using just one of the still images, it was determined that the horses hooves left the ground when they were all tucked under the horse, as it switches from ‘pulling’ from the front legs to ‘pushing’ from the back legs.
Etienne-Jules Marey expanded on Muybridges work in developing animated photography, which sought to record several phases of movement on one photographic surface. With the development of this new method, Marey pioneered modern motion analysis. Marey also invented the first motion capture suit, which consisted of a black tight fitting suit with metal strips or white lines attached to it (Figure 2). With this suit and Marey’s method of recording multiple frames on one photographic plate, the beginnings of motion capture and motion analysis can easily be seen.

4. Applications of Motion Capture
Entertainment (Movies, Games)
Medical Field
Sports Analysis and Enhancement
Industrial Field
Increase process efficiency, product design, ergonomics evaluation
Research and Defense
Entertainment Industry – such as Movies, and games – is perhaps the most visible and recognized user of motion capture technology
-Through the use of motion capture, films can simulate or approximate the look of live-action cinema with nearly photorealistic digital character models
- there is a bit of a dispute between computer animation studios that use motion capture, and those that do not. One of the debates is over the artistic qualities that motion capture seems to sidestep through the use of technology. Some artists feel as though creating a film purely via motion capture is cheating.
Medical Field – It can assist with diagnosis and treatment of physical ailments. The motion capture data can also help show progression of the patient. The data from previous sessions can be compared real-time to see if the rehabilitation approach is having a desired result.
Motion capture is ideal for a wide range of sports applications in research, education, training, and enhancement. With sports becoming more and more competitive and performance always increasing, motion capture can be used to break down every detail of a certain motion in order to make performance enhancing modifications.
The Industrial field and the research and defense field are starting to use motion capture more and more for better, quicker, and faster analysis and design – among other things.

5. Approaches to Motion Capture
Marker Based
Goniometers
Magnetic
Inertial
Opitcal
Goniometers - A goniometer is an instrument that measures angles. A sort of exoskeleton is strapped to the subjects body, that moves with the rigid bodies of the subject. The model can then be built based on this angle data.
A big issue with this approach is the exoskeleton can greatly hinder the free movement of the subject.

6. Magnetic
Magnetic - motion capture systems utilize sensors placed on the body to measure the low-frequency magnetic fields generated by a transmitting source. The electronic control units are networked with a host computer that uses a software driver to represent these positions and rotations in 3D space.
Issues with magnetic motion capture include the fact that magnetic fields decrease in power rapidly as the distance from the generating source increases - so capture area is very minimized. Also great care has to be taken since any magetic material in the capture space can have a negative effect on the results.

7. Inertial
Inertial - Advances in miniaturized and micro machined accelerometers and rate sensor technology has made this approach possible. . The sensors, located at predetermined anatomical points on the body, provide angular and force data at each point.
Issues with this approach include problems with the sophistication and cost of the sensors, as well as Noise and bias errors are common.

8. Optical
Optical – which is probably the most common approach uses reflective (passive) or strobing (active) markers placed at predetermined anatomical positions on the subject. Multiple cameras track the markers and which are aligned with boney landmarks on the subject and determine anatomical points to recreate the subject as a 3D model.
The biggest issue with optical markers is occlusion of markers – and markers falling off.
Upper left – set of Kin Kong and upper right is set of I Robot
So – as a result of the issues associated to the markered approaches, the latest trend in Motion capture is developing markerless motion capture systems.

9. MIT’s Pfinder Markerless Approach
2D Blob Model
One of the earlier approaches to markerless motion capture was by a group at MIT who developed a system called Pfinder (or Person Finder) in For its time - Pfinder provided an accurate method for person segmentation, tracking and interpretation, and creating 2D models of human bodies from a single camera video. Pfinder uses features called ‘blobs’ connected together to form the human model. Each blob has a spatial (x,y) and color distribution, as well as a support map that indicates which pixels of the image are members of that blob. The capturing process begins with the system learning the background (with no human in it) - The scene is modeled as a texture surface; each point on the texture surface is associated with a mean color value and a distribution about that mean. In each frame the visible portions of the scene are recursively updated, which compensates for objects of the scene being covered by the human.
The changed region of the image is found, and analyzed in order to begin to building a blob model of the person. Pfinder utilizes a 2D contour shape analysis process that attempts to identify the head, hands and feet locations. If one of these locations is discovered a new blob is placed in the location.
In today’s standards Pfinder would be considered very crude. Additionally, this approach did not track quick motion well. Sudden changes in the scene were often mistakenly rendered.

10. Stanford Markerless Motion Capture Project
3D voxel reconstruction from silhouettes
One of the most well known current leading project in markerless motion capture is the Stanford Markerless Motion Capture Project - , their approach relies on images obtained from multiple synchronized cameras placed around the subject to estimate the pose of the subject. The process includes utilizing 2D foreground silhouette data from the original images, as well as 3D data in the form of voxels. The foreground silhouette data is obtained from the original images by performing background subtraction. A voxel model is created by projecting the foreground silhouettes of the 2D images onto a 3D grid of points. A point in the grid is considered a voxel if it lies inside the silhoutte of all the images.
Next the 3D voxel data is segmented into the different body parts, then 1D splines are fitted to the principle axis of each of the body parts. The position of each body segment is derived in terms of the position of its attached coordinate frame with respect to its parent segment and is represented by a transformation matrix which includes a rotational component and a translational component. AS you can see from (c) the trunk is used as the base coordinated system and all of the segments link back to it.
So at this point we have created an accurate 3D skeleton model – as seen in (d) above.
The next step is to estimate the super-quadric parameters of the body segments in order to build the 3D super-quadric model (e). The shape of the super-quadric model is determined by maximizing the overlap between the super-quadric model and the voxels. This is done by minimizing the distance between each voxel and the super-quadric model. Voxels at the surface of the super-quadric have a distance equal to zero (d0). The distances for voxels inside the super-quadric have a negative distance so that if the voxel is on the axis of the super-quadric it has a value of d-1. For voxels that are outside the super-quadric, the distance increases exponentially as the voxel is farther from the surface. The optimal pose of the super-quadric model is that which minimizes d.

11. Human Shape and Pose from Images Using SCAPE
SCAPE Models
Template
Body Pose
Another approach to markerless motion capture is being developed by a team of members from Brown University, UC Santa Cruz, and Intel Research. A big difference in their approach over other approaches is that they are able to skip the voxel representation of the model. They are able to show that a detailed parametric model can be estimated directly from the image data.
The first phase, which is the learning phase, utilizes the SCAPE (Shape Completion and Animation of People) model. The SCAPE model basically utilizes a large database of preexisting human models. The data for the models were captured using a Cyberware WBX whole-body scanner. The scanner captures range scans from four directions simultaneously and the models contain approximately 200K points. These points are used to create two types of mesh models: a pose set, which contains scans of 70 poses, and a body shape set, which contains scans of 37 different people in a similar pose. A single pose model is marked as the template model and all other models are brought into correspondence with that model through Correlated Correspondence. This produces a set of meshes with the same topology, so that a single model can easily be morphed into different shapes and poses.
An algorithm is used to automatically compare these different meshes and extract a proper skeletal configuration. The result is a tree-structured articulated skeleton with 16 parts. (as depicted in the far right column on the picture) The next step is to calculate the deformations between the template and the other mesh models. This basically gives the formula for transforming the template into the other meshes. This is done for both the pose and body shape sets. Next, an algorithm is used to ‘learn’ the coefficients of a linear mapping between poses and shapes. This linear mapping is what is used to predict a new body pose, not present in the database.
Body Shape

12. Human Shape and Pose from Images Using SCAPE
Input Images overlaid with estimated body model
Overlap (yellow) between silhouette (red) and estimated model (blue)
In order to translate image data to a model in the SCAPE database, silhouettes are used to create a crude model similar to what was described in the Stanford project. However, unlike the Stanford project, a voxel model is not created. Rather, the joint angles together with the mean body shape parameters of the silhouette model are used to initialize a stochastic search of the SCAPE parameters. A cost function is used to measure how well a hypothesized model fits image observations. The silhouette model is compared to a generated silhouette of the SCAPE model. Pixels are penalized in non-overlapping regions in one silhouette by the shortest distance to the other silhouette and vice-versa. This is done by producing a Chamfer distance map for each silhouette. The predicted silhouette (SCAPE model) should not exceed the image silhouette, while at the same time try to cover as much as possible.
Recovered model from each camera view

13. Future Trends of Motion Capture
Markerless Motion Capture
In general –
Increased speed
Increased capture area
Decreased cost
Multiple character motion capture

14. Resources Markerless Motion Capture: Goniometer Motion Capture
Organic Motion –
Mova –
MaMoCa –
Optical Motion Capture:
VICON –
MotionAnlysis –
OptiTrack –
Qualisys Motion Capture Systems –
PhaseSpace Motion Capture –
Goniometer Motion Capture
Animazoo –
Magnetic Motion Capture:
Ascension -
Polhemus –
Inertial Motion Capture:
Animazoo –
InterSense –
Moven –
Motion analysis software:
Autodesk – – 3-D CAD and animation software.
C-Motion, Inc. – – 3-D motion analysis software.

15. References
[1] Struman, David, A Brief History of Motion Capture for Computer Character Animation, found on the web:
[2] Furniss, Maureen, Motion Capture, found on the web:  
[3] Human Motion Analysis, found on the web:
[4] Eadweard Muybridge, from Wikipedia, found on the web:
[5] Adventures in Cybersound, found on the web:
[6] Animazoo, found on the web:  
[7] Motion capture, from Wikipedia, found on the web:
[8] A.G. Kirk, J.F. O'Brien, D.A. Forsyth, Skeletal Parameter Estimation From Optical Motion Capture Data, Computer Vision and Pattern Recognition, CVPR IEEE Computer Society Conference Volume 2, June 2005 Page(s): 782–788. 
[9] Meta Motion, found on the web:
[10] J.F. O’Brien, R.E Bodenheimer, Jr., G.J. Brostow, J.K. Hodgins, Automatic Joint Parameter Estimation from Magnetic Motion Capture Data, Graphics Interface 2000. 
[11] H. Zhou, H. Hu, Kinematic model aided inertial motion tracking of human upper limb, IEEE Conference on Information Acquisition, June27-July3, 2005. 
[12] Moven, found on the web:  
[13] Organic Motion Launches Markerless Motion Capture System, Studio Daily ( August 9, 2007. 
[14] Organic Motion, found on the web:  
[15] L. Mundermann, S. Corazza, T.P. Andriacchi, The Evolution of Methods for the Capture of Human Movement Leading to Markerless Motion Capture for Biomechanical Applications, Department of Mechanical Engineering, Stanford University, 2006. 
[16] C. Wren, A. Azarbayejani, T. Darrell, A. Pentland, Pfinder: Real-Time Tracking of the Human Body, IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 19, Issue 7, Jul 1997 Page(s): 780–785. 
[17] A. Sundaresan, Towards Markerless Motion Capture: Model Estimation, Initialization and Tracking, University of Maryland, 2007. 
[18] Stanfords Markerless Motion Capture Project, found on the web:  
[19] S. Corazza, L. Mundermann, A.M. Chaudhari, T. Demattio, C. Cobelli, T.P. Andriacchi, A Markerless Motion Capture System to Study Musculoskeletal Biomechanics: Visual Hull and Simulated Annealing Approcah, Annals of Biomedical Engineering, Vol. 34, No. 6, June 2006, Page(s):  
[20] A.O. Balan, L. Sigal, M. Black, J.E. Davis, H.W. Haussecker, Detailed Human Shape and Pose from Images, IEEE Conference on Computer Vision and Pattern Recognition, 17-22, June 2007 Page(s): 1-8. 
[21] D. Anguelov, P. Srinivasan, D. Koller, S. Thrun, J. Rodgers, J. Davis, SCAPE: Shape Completion and Animation of People, ACM Trans. Graphics, 24(3): , 2005. 
[22] Motion Analysis, found on the web:
[23] PhaseSpace, found on the web:
[24] Improving Quality with Virtual Technology, found on the web:

16. Questions?