Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Non-Rigid Structure from Motion using non-Parametric Tracking and Non-Linear Optimization
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 1 - Volume 01
A Measure of Deformability of Shapes, with Applications to Human Motion Analysis
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Motion segmentation with missing data using power factorization and GPCA
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Towards a measure of deformability of shape sequences
Pattern Recognition Letters
Rotation constrained power factorization for structure from motion of nonrigid objects
Pattern Recognition Letters
Segmentation of Rigid Motion from Non-rigid 2D Trajectories
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Non-rigid metric reconstruction from perspective cameras
Image and Vision Computing
Deformation weight constraint and 3D reconstruction of nonrigid objects
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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Non-rigid 3D shape recovery is an inherently ambiguous problem. Given a specific rigid motion, different non-rigid shapes can be found that fit the measurements. To solve this ambiguity prior knowledge on the shape and motion should be used to constrain the solution. This paper is based on the observation that often not all the points on a moving and deforming surface such as a human face are undergoing non-rigid motion. Some of the points are frequently on rigid parts of the structure – for instance the nose – while others lie on deformable areas. First we develop a segmentation algorithm to separate rigid and non-rigid motion. Once this segmentation is available, the rigid points can be used to estimate the overall rigid motion and to constrain the underlying mean shape. We propose two reconstruction algorithms and show that improved reconstructions can be obtained when the priors on the shape are used on synthetic and real data.