A model for data in motion

Original 3D motion sequences lie in high dimensional subspace and on a high-dimensional manifold which is highly contorted, so it is difficult to cluster the similar poses together to form distinct movements. Here we use a non-linear learning ...

We consider the problem of segmenting multiple rigid-body motions from point correspondences in multiple affine views. We cast this problem as a subspace clustering problem in which point trajectories associated with each motion live in a linear ...

We propose motion manifold learning and motion primitive segmentation framework for human motion synthesis from motion-captured data. High dimensional motion capture date are represented using a low dimensional representation by topology preserving ...