My Research interests are: 

Optical Markerless Motion Capture and Pose Estimation

2D-3D Pose Estimation

Real-Time Tracking and  Multidisplay Rendering

Augmentation (AR), Panoramas and Real-Time Graphics

Articulated ICP from Stereo Images (see PhD thesis)
To apply the developed pose estimation algorithm,  correspondences between observed 3D points and 3D model points are necessary. As the observed points are calculated from depth maps, these correspondences are not known. Similar to other tracking systems an Iterative Closest Point (ICP) approach is taken. For each observed point, the nearest point on the model is assumed to be the corresponding one. With these correspondences the body pose of the model is calculated.
The following steps are repeated multiple times for each frame of a sequence. The larger the relative motion is, the more correspondences will be incorrect. The principle idea of ICP is, that an estimated relative motion decreases the difference between model and observation, such that more correspondences are correct, if built again for the smaller difference in the next iteration.
The steps in one ICP iteration are:
a Render model in current pose with a unique color for each triangle (OpenSG).
b Find visible model points by analyzing the color of rendered image pixels.
c Build correspondences from the depth map by finding nearest model point.
d Estimate relative motion from 3D-point-3D-point correspondences by  Nonlinear Least  Squares.

Two views on the stereo input data(white boxes) for one image frame.
The articulated ICP estimates joint angles, such the surface
of the human model is fitting as close as possible to the input data.
Correspondences are found by using the closest model point for each input point.
Video(17MB wmv): The resulting sequence (24 DOF).

Human Models

There are a lot of different human models with different movement capabilities, where usually each one is designed for a spcecific case. Though no commonly used standard of human animation models exists, the MPEG group defines a rather general model in the MPEG-4 specification , which is basically the H-Anim body specification. The H-Anim model is the basis for my work and gives the opportunity to use MPEG4-BAP-files (Body Animation Parameter) for animating characters.
The images show two H-Anim models in VRML format, which can be loaded and viewed by openSG.

For tracking in real-time all 180 degrees of freedom in the MPEG-4 body model are not necessary. Additionally the geometry of the models should be as simple as possible for fast processing of visible points, building correspondences etc.

Therefore we made a simple body model ourselve, that has 39 degrees of freedom, as shown on the right. The amount of triangles is also greatly reduced as compared to the one on the left.
The possible movement capabilities are shown as arrows,
each arrow represents a rotational joint axis. The key of our approach is, that the motion can be described by rotational angles around known axes sufficiently.


     simple Human

Model Fitting

To track the motion of a specific person, it is necessary in our approach to have a model of the person that fits as good as possible. We developed a fitting algorithm that  estimates scale values for each body part from a template model. The fitting is necessary for each person. We are using only one single camera (see publications: Human Model Fitting from Monocular Posture Images).
To overcome the the lack of depth information (single camer), the person has to strike six different postures as shown on the right.
The approach is based on silhouette information and minimizes differences between model and template silhouettes by  non-linear least squares minimization solved with the Levenberg-Marquardt method(up to 100 parameters). The optimization estimates parameters for all 6 images simultaneously.

fitting the model