## Automatic Registration for Articulated Shapes |
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University of California, San Diego |
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## AbstractWe present an unsupervised algorithm for aligning a pair of shapes in the presence of significant articulated motion and missing data, while assuming no knowledge of a template, user-placed markers, segmentation, or the skeletal structure of the shape. We explicitly sample the motion, which gives a priori the set of possible rigid transformations between parts of the shapes. This transforms the problem into a discrete labeling problem, where the goal is to find an optimal assignment of transformations for aligning the shapes. We then apply graph cuts to optimize a novel cost function, which encodes a preference for a consistent motion assignment from both source to target and target to source. We demonstrate the robustness of our method by aligning several synthetic and real-world datasets. ## Downloads## Paper
[ Paper ] (16.3 MB Adobe PDF, Hi-Res)
## Talk Slides[ Slides ] (15.7 MB Powerpoint Presentation) |