Video Time-Space Body Filtering Based on Dual-Directional Markov Chain Monte Carlo Particle Filter

Abstract

Nowadays with the blooming development of new media and cultural creative industries, the video animation has drawn so many researchers’ attention due to animation production efficiency and animation expression. This paper aims at the difficulty that lack of observation model, proposes a method of video time-space body filtering based on dual-directional Markov chain Monte Carlo particle filter for video motion redirection. At first, after extracting the key frame of a given video, by affine transformation and linear superposition, the subject initializes the video’s time-space parameters and forms the observation model; Secondly, in each time-space body, based on the bi-directional Markov property of each frame, this paper proposed a dual-directional Markov chain Monte Carlo sampling particle filter structure and takes full advantage of the relationship of the front and back frame of the parameters to estimate motion redirection parameters. The research of this paper will produce an efficient and prominent animation expressive video motion redirection method and play an important role on video animation of the development.