Abstract
In this paper, we address the problem of estimating the 3D structure and motion of a non-rigid object based on feature points throughout a image sequence. The main limitation of existing factorization methods is that they are difficult to provide correct structure and motion estimates: the motion matrix has a repetitive structure which is not represented by these methods. In order to cope with this problem, we formulate the 3D non-rigid shape as a linear combination of basis trajectories which are represented by the Discrete Cosine Transform (DCT). Based on this, a framework of Markov random field (MRF) with constraints is proposed. By incorporating the motion prior constraints into the MRF, the motion smoothness features between consecutive image frames and local regions are reflected. Finally, the motion and shape estimates are achieved by a non-linear optimization method. Experimental results from a talking face image sequence demonstrate the feasibility of the proposed approach.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.