Rotation constrained power factorization for structure from motion of nonrigid objects

The paper addresses the problem of recovering 3D structure and motion of nonrigid objects from image sequences. We propose a rotation constrained power factorization (RCPF) algorithm that combines the orthonormality and the replicated block structure of the motion matrix directly into iterations. The algorithm overcomes some limitations of previous SVD-based methods and can work with missing data. Based on the shape bases of the batch-type factorization, we also propose a sequential factorization technique to recover the shape and motion of new frames conveniently. Extensive experiments show the effectiveness of the proposed algorithm.