A Bayesian Framework for Recursive Object Removal in Movie Post-Production

Abstract

Some of the most convincing film and video effects are created in digital post-production by removing apparatus that supports or manipulates actors and objects. Wires and people, for instance, can be removed by digitally painting them out of the scene provided some 'clean plate' image is available for pasting in the missing regions. This paper addresses the problem when no such plate is available. Object removal requires the estimation of the motion of the hidden material and then the reconstruction of the missing image data. Using the notion of temporal motion smoothness, this paper articulates the two problems using a Bayesian framework and so develops a unique tool for automated object removal. The tool is currently being tested in the film effects industry and initial feedback is very positive.