We use Scaled Gaussian Process Latent Variable Models (SGPLVM) to learn prior models of 3D human pose for 3D people
tracking. The SGPLVM simultaneously optimizes a low dimensional embedding of the high-dimensional pose data and a density function that both gives higher probability to points close to training data and provides a nonlinear probabilistic mapping from the low-dimensional latent space to the full-dimensional pose space. The SGPLVM is a natural choice when only small amounts of training data are available.
We demonstrate our approach with two distinct motions, golfing and walking.
We show that the SGPLVM sufficiently constrains the problem such that tracking can be accomplished with straighforward deterministic optimization.

We propose an approach to incorporating dynamic models into the human body tracking process that yields full 3--D reconstructions from monocular sequences.
We formulate the tracking problem in terms of minimizing a differentiable criterion whose differential structure is rich enough for successful optimization using a simple hill-climbing approach as opposed to a multi-hypotheses probabilistic one. In other words, we avoid the computational complexity of multi-hypotheses algorithms while obtaining excellent results under challenging conditions.

To demonstrate this, we focus on monocular tracking of a golf
swing from ordinary video. It involves both dealing with
potentially very different swing styles, recovering arm motions that are perpendicular to the camera plane and handling strong self-occlusions.

We use temporal motion models based on Principal Component Analysis (PCA) to formulate the tracking problem as one of minimizing differentiable objective functions. Our experiments show that the differential structure of these objective functions is rich enough to take advantage of standard deterministic optimization methods, whose computational requirements are much smaller than those of probabilistic ones and can nevertheless yield very good results even in difficult situations.

We show the effictiveness of our approach compare to probabilistic approaches, that while effective, require exponentially large amounts of computation as the number of degrees of freedom in the model increases.

Representing motions as linear sums of principal components has become a widely accepted animation technique. While powerful, the simplest version of this approach is not particularly well suited to modeling the specific style of an individual whose motion had not yet been recorded when building the database: It would take an expert to adjust the PCA weights to obtain a motion style that is indistinguishable from his. Consequently, when realism is required, current practice is to perform a full motion capture session each time a new person must be considered.

We extend the PCA approach so that this requirement can be drastically reduced: For whole classes of motion such as walking or running, it is enough to observe the newcomer moving only once at a particular speed using either an optical motion capture system or a simple pair of synchronized video cameras. This one observation is used to compute a set of principal component weights that best approximates the motion and to extrapolate in real-time realistic animations of the same person walking or running at different speeds.

Our goal is to improve a joint limits representation that has the following characteristics:

1. captures intra- and inter-joint coupling,
2. is easy to derive on the basis of live motion recordings,
3. allows rapid determinant of the validity of a rotation.

Two main applications to a hierarchical modelization of a set of joint limits are shown: Video-based motion capture and animation