Sparse Localized Deformation Components
MetainformationenACM Transactions on Graphics 32 (6), (Proceedings of SIGGRAPH Asia). 2013
We propose a method that extracts sparse and spatially localized deformation modes from an animated mesh sequence. To this end, we propose a new way to extend the theory of sparse matrix decompositions to 3D mesh sequence processing, and further contribute with an automatic way to ensure spatial locality of the decomposition in a new optimization framework. The extracted dimensions often have an intuitive and clear interpretable meaning. Our method optionally accepts user-constraints to guide the process of discovering the underlying latent deformation space. The capabilities of our efficient, versatile, and easy-to-implement method are extensively demonstrated on a variety of data sets and application contexts. We demonstrate its power for user friendly intuitive editing of captured mesh animations, such as faces, full body motion, cloth animations, and muscle deformations. We further show its benefit for statistical geometry processing and biomechanically meaningful animation editing. It is further shown qualitatively and quantitatively that our method outperforms other unsupervised decomposition methods and other animation parameterization approaches in the above use cases.
We'd like to thank Thabo Beeler, Li Zhang, Levi Valgaerts, Chenglei Wu and Nils Hasler for making their captured datasets available. Maryam Mustafa kindly helped with the voice-over for the videos. We also thank the anonymous reviewers, who provided very valuable feedback and useful comments on how to improve the paper.