Recent works have shown that 3D shape of non-rigid surfaces can be accurately retrieved from a single im- age given a set of 3D-to-2D correspondences between that image and another one for which the shape is known. However, existing approaches assume that such correspon- dences can be readily established, which is not necessarily true when large deformations produce significant appear- ance changes between the input and the reference images. Furthermore, it is either assumed that the pose of the cam- era is known, or the estimated solution is pose-ambiguous. In this paper we relax all these assumptions and, given a set of 3D and 2D unmatched points, we present an approach to simultaneously solve their correspondences, compute the camera pose and retrieve the shape of the surface in the input image. This is achieved by introducing weak priors on the pose and shape that we model as Gaussian Mixtures. By combining them into a Kalman filter we can progressively reduce the number of 2D candidates that can be potentially matched to each 3D point, while pose and shape are refined. This lets us to perform a complete and efficient exploration of the solution space and retain the best solution
