Markov Chains Fusion for Video Scene Generation

In this paper we address the general issue of merging Markov chains used to model two instances of a given process with some properties in common. In particular, in this work we apply this scenario to a multimedia application that generates new video scenes mixing the original segments of a given movie. To perform the latter process, it is first necessary to describe the structure of the scenes in some way, which in our case is done through Markov chains. The video scenes are then recombined by fusing their corresponding models using the general method described here. We analyze and validate the proposed methodology only for this specific application, however the solution presented here could be used in a very diverse array of applications where Markov chains are routinely used, ranging from queuing modeling to financial decision processes

Interactive Film Recombination

In this paper we discuss an innovative media entertainment application called Interactive Movietelling. As an offspring of Interactive Storytelling applied to movies, we propose to integrate narrative generation through AI planning with video processing and modeling to construct filmic variants starting from the baseline content. The integration is possible thanks to content description using semantic attributes pertaining to intermediate-level concepts shared between video processing and planning levels. The output is a recombination of segments taken from the input movie performed so as to convey an alternative plot. User tests on the prototype proved how promising Interactive Movietelling might be, even if it was designed at a proof of concept level. Possible improvements that are suggested here lead to many challenging research issues