Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
A Sequential Factorization Method for Recovering Shape and Motion From Image Streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonrigid motion analysis: articulated and elastic motion
Computer Vision and Image Understanding
A Factorization Based Algorithm for Multi-Image Projective Structure and Motion
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Human Motion: Modeling and Recognition of Actions and Interactions
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
Articulated Structure from Motion by Factorization
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A 3D Facial Expression Database For Facial Behavior Research
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Non-rigid structure from motion using ranklet-based tracking and non-linear optimization
Image and Vision Computing
Rotation constrained power factorization for structure from motion of nonrigid objects
Pattern Recognition Letters
Nonrigid Structure-from-Motion: Estimating Shape and Motion with Hierarchical Priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Camera Smoothing in Sequential Structure-from-Motion Using Approximate Cross-Validation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Perspective Nonrigid Shape and Motion Recovery
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Estimating 3D shape from degenerate sequences with missing data
Computer Vision and Image Understanding
Generic and real-time structure from motion using local bundle adjustment
Image and Vision Computing
Rigid Structure from Motion from a Blind Source Separation Perspective
International Journal of Computer Vision
Sequential non-rigid structure-from-motion with the 3D-implicit low-rank shape model
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-rigid structure from motion with complementary rank-3 spaces
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Nonrigid Structure-From-Motion From 2-D Images Using Markov Chain Monte Carlo
IEEE Transactions on Multimedia
Kernel non-rigid structure from motion
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Hi4D-ADSIP 3-D dynamic facial articulation database
Image and Vision Computing
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Most existing approaches in structure from motion for deformable objects focus on non-incremental solutions utilizing batch type algorithms. All data is collected before shape and motion reconstruction take place. This methodology is inherently unsuitable for applications that require real-time learning. Ideally the online system is capable of incrementally learning and building accurate shapes using current measurement data and past reconstructed shapes. Estimation of 3D structure and camera position is done online. To rely only on the measurements up until that moment is still a challenging problem. In this paper, a novel approach is proposed for recursive recovery of non-rigid structures from image sequences captured by a single camera. The main novelty in the proposed method is an adaptive algorithm for construction of shape constraints imposing stability on the online reconstructed shapes. The proposed, adaptively learned constraints have two aspects: constraints imposed on the basis shapes, the basic ''building blocks'' from which shapes are reconstructed; as well as constraints imposed on the mixing coefficients in the form of their probability distribution. Constraints are updated when the current model no longer adequately represents new shapes. This is achieved by means of Incremental Principal Component Analysis (IPCA). The proposed technique is also capable to handle missing data. Results are presented for motion capture based data of articulated face and simple human full-body movement.