Progressive shape models

In this paper we address the problem of recovering both the topology and the geometry of a deformable shape using temporal mesh sequences. The interest arises in multi-camera applications when unknown natural dynamic scenes are captured. While several approaches allow recovery of shape models from static scenes, few consider dynamic scenes with evolving topology and without prior knowledge. In this nonetheless generic situation, a single time observation is not necessarily sufficient to infer the correct topology of the observed shape and evidences must be accumulated over time in order to learn the topology and to enable temporally consistent modelling. This appears to be a new problem for which no formal solution exists. We propose a principled approach based on the assumption that the observed objects have a fixed topology. Under this assumption, we can progressively learn the topology meanwhile capturing the deformation of the dynamic scene. The approach has been successfully experimented on several standard 4D datasets.