Abstract

We present a novel methodology for the analysis of com- plex ob ject shapes in motion observed by multiple video cameras. In particular, we propose to learn local surface rigidity probabilities (i.e., deformations), and to estimate a mean pose over a temporal sequence. Local deformations can be used for rigidity-based dynamic surface seg- mentation, while a mean pose can be used as a sequence keyframe or a cluster prototype and has therefore numerous applications, such as motion synthesis or sequential alignment for compression or morphing. We take advantage of recent advances in surface tracking techniques to formulate a generative model of 3D temporal sequences using a prob- abilistic framework, which conditions shape fitting over all frames to a simple set of intrinsic surface rigidity properties. Surface tracking and rigidity variable estimation can then be formulated as an Expectation- Maximization inference problem and solved by alternatively minimizing two nested fixed point iterations. We show that this framework pro- vides a new fundamental building block for various applications of shape analysis, and achieves comparable tracking performance to state of the art surface tracking techniques on real datasets, even compared to ap- proaches using strong kinematic priors such as rigid skeletons.