Batch Algorithm with Additional Shape Constraints for Non-Rigid Factorization

Recently, recovery of non-rigid structure by the factorization algorithms have received attention in the literature. The factorization algorithm decomposes the feature points over the given image sequence into motion of the camera and 3D shape bases. The non-rigid structure can be represented by the linear combination of the 3D shape bases. Although the closed-form solution of the non-rigid factorization algorithm is proven, the algorithm is sensitive to noise. In this paper, we propose a batch algorithm to recover multiple non-rigid structures from subsets of the data. Then, we introduce a set of non-linear shape constraints to optimize the recovered non-rigid structures. Synthetic data and real data were used in the experiments. The experimental results showed that the new factorization algorithm gives significant improvement than the original algorithm. With noisy data, the new algorithm is more robust and more accurate in recovering non-rigid structure.