Artificial neural networks for 3D nonrigid motion analysis

Abstract

A novel approach to 3D nonrigid motion analysis using artificial neural networks is presented. A set of neural networks is proposed to tackle the problem of nonrigidity in 3D motion estimation. Constraints are specified to ensure a stable and global consistent estimation of local deformations. The assignments of weights between two layers, the initial values of the outputs, and the connections between each network reflect the constraints defined. The objective of the proposed neural networks is to find the optimal deformation matrices that satisfy the constraints for all the points on the surface of the nonrigid object. Experimental results on synthetic and real data are provided.<>