Estimation of Dense, Non-rigid Motion Fields from a Multi-camera Array Using a Hierarchical Mixture Model

Abstract

The problem of modelling objects of arbitrary complecity for video based rendering has been much studied in recent years, with the growing interest in ‘free viewpoint’ video and similar applications. Typical approaches fall into two categories: those which approximate surfaces from dense depth maps obtained by generalisations of stereopsis methods and those which employ an explicit geometric representation such as a mesh. While the former has generality with respect to geometry, it is inevitably limited in terms of viewpoint; the latter, on the other hand, sacrifices generality of object geometry for freedom to pick an arbitary viewpoint. The purpose of the work reported here is to bridge this gap in object representation, by employing a surface element model, but one which is freed from the restrictions of a mesh. Estimation of the model and tracking it through time from multiple cameras is achieved by novel multiresolution stochastic simulation methods. After a brief outline of the method, its use in modelling human motions using data from the Warwick multi-camera studio is presented to illustrate its effectiveness compared to the current state of the art.