The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Trainable videorealistic speech animation
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Universal capture - image-based facial animation for "The Matrix Reloaded"
SIGGRAPH '05 ACM SIGGRAPH 2005 Courses
Capturing and animating occluded cloth
ACM SIGGRAPH 2007 papers
An Improved Algorithm for TV-L1 Optical Flow
Statistical and Geometrical Approaches to Visual Motion Analysis
High resolution passive facial performance capture
ACM SIGGRAPH 2010 papers
Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Database and Evaluation Methodology for Optical Flow
International Journal of Computer Vision
High-quality passive facial performance capture using anchor frames
ACM SIGGRAPH 2011 papers
Robust trajectory-space TV-L1 optical flow for non-rigid sequences
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Feature-Based Deformable Surface Detection with Self-Occlusion Reasoning
International Journal of Computer Vision
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Tracking through long image sequences is a fundamental research issue in computer vision. This task relies on estimating correspondences between image pairs over time where error accumulation in tracking can result in drift. In this paper, we propose an optimization framework that utilises a novel Anchor Patch algorithm which significantly reduces overall tracking errors given long sequences containing highly deformable objects. The framework may be applied to any tracking algorithm that calculates dense correspondences between images, e.g. optical flow. We demonstrate the success of our approach by showing significant tracking error reduction using 6 existing optical flow algorithms applied to a range of benchmark ground truth sequences. We also provide quantitative analysis of our approach given synthetic occlusions and image noise.