Spatio-Temporal Stereo Using Multi-Resolution Subdivision Surfaces
International Journal of Computer Vision
International Journal of Computer Vision
Spacetime faces: high resolution capture for modeling and animation
ACM SIGGRAPH 2004 Papers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-View Stereo Reconstruction and Scene Flow Estimation with a Global Image-Based Matching Score
International Journal of Computer Vision
Surface Capture for Performance-Based Animation
IEEE Computer Graphics and Applications
Articulated mesh animation from multi-view silhouettes
ACM SIGGRAPH 2008 papers
Efficient reconstruction of nonrigid shape and motion from real-time 3D scanner data
ACM Transactions on Graphics (TOG)
High-quality passive facial performance capture using anchor frames
ACM SIGGRAPH 2011 papers
Cooperative patch-based 3D surface tracking
CVMP '11 Proceedings of the 2011 Conference for Visual Media Production
Animation cartography—intrinsic reconstruction of shape and motion
ACM Transactions on Graphics (TOG)
Dense and accurate spatio-temporal multi-view stereovision
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Global temporal registration of multiple non-rigid surface sequences
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Global Non-rigid Alignment of Surface Sequences
International Journal of Computer Vision
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This paper addresses the problem of optimal alignment of non-rigid surfaces from multi-view video observations to obtain a temporally consistent representation. Conventional non-rigid surface tracking performs frame-to-frame alignment which is subject to the accumulation of errors resulting in drift over time. Recently, non-sequential tracking approaches have been introduced which re-order the input data based on a dissimilarity measure. One or more input sequences are represented in a tree with reducing alignment path length. This limits drift and increases robustness to large non-rigid deformations. However, jumps may occur in the aligned mesh sequence where tree branches meet due to independent error accumulation. Optimisation of the tree for non-sequential tracking is proposed to minimise the errors in temporal consistency due to both the drift and jumps. A novel cluster tree enforces sequential tracking in local segments of the sequence while allowing global non-sequential traversal among these segments. This provides a mechanism to create a tree structure which reduces the number of jumps between branches and limits the length of branches. Comprehensive evaluation is performed on a variety of challenging non-rigid surfaces including faces, cloth and people. This demonstrates that the proposed cluster tree achieves better temporal consistency than the previous sequential and non-sequential tracking approaches. Quantitative ground-truth comparison on a synthetic facial performance shows reduced error with the cluster tree.