Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Shape and Nonrigid Motion Estimation Through Physics-Based Synthesis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Meshless deformations based on shape matching
ACM SIGGRAPH 2005 Papers
Capturing and animating occluded cloth
ACM SIGGRAPH 2007 papers
Perspective Nonrigid Shape and Motion Recovery
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Piecewise quadratic reconstruction of non-rigid surfaces from monocular sequences
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Linear Local Models for Monocular Reconstruction of Deformable Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Monocular template-based reconstruction of smooth and inextensible surfaces
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
IEEE Transactions on Pattern Analysis and Machine Intelligence
Energy based multiple model fitting for non-rigid structure from motion
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Physically-based motion models for 3D tracking: A convex formulation
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Kernel non-rigid structure from motion
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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This paper addresses the problem of reconstructing a deforming surface from point observations in a monocular video sequence. Recent state-of-the-art approaches divide the surface into smaller patches to simplify the problem. Among these, one very promising approach reconstructs the patches individually using a quadratic deformation model. In this paper, we demonstrate limitations that affect its applicability to real-world data and propose an approach that overcomes these problems. In particular, we show how to eliminate the need for manually picking a template that is used to model the deformations. We evaluate our algorithm on both synthetic and real-world data sets and show that it systematically reduces the reconstruction error by a factor of up to ten.