  We develop in this paper a generic Bayesian framework for the joint
estimation of motion and recovery of missing data in a damaged video sequence.
Using standard maximum a posteriori to variational formulation rationale, we
derive generic minimum energy formulations for the estimation of a
reconstructed sequence as well as motion recovery. We instantiate these energy
formulations and from their Euler-Lagrange Equations, we propose a full
multiresolution algorithms in order to compute good local minimizers for our
energies and discuss their numerical implementations, focusing on the missing
data recovery part, i.e. inpainting. Experimental results for synthetic as well
as real sequences are presented. Image sequences and extra material is
available at http://image.diku.dk/francois/seqinp.php.
