Yotam Gingold (George Mason University)
In example-based inverse linear blend skinning (LBS), a collection of poses (e.g., animation frames) are given, and the goal is finding skinning weights and transformation matrices that closely reproduce the input. These poses may come from physical simulation, direct mesh editing, motion capture, or another deformation rig. We describe a re-formulation of inverse skinning as a problem in high-dimensional Euclidean space. The transformation matrices applied to a vertex across all poses can be thought of as a point in high dimensions. We cast the inverse LBS problem as one of finding a tight-fitting simplex around these points (a well-studied problem in hyperspectral imaging). Although we do not observe transformation matrices directly, the 3D position of a vertex across all of its poses defines an affine subspace, or flat. We solve a “closest flat” optimization problem to find points on these flats, and then compute a minimum-volume enclosing simplex whose vertices are the transformation matrices and whose barycentric coordinates are the skinning weights. We are able to create LBS rigs with state-of-the-art reconstruction error, and state-of-the-art compression ratios for mesh animation sequences. Our solution does not consider weight sparsity or the rigidity of recovered transformations. We include observations and insights into the closest flat problem. Its ideal solution, and optimal LBS reconstruction error, remain an open problem.
Bio: Yotam Gingold is an Associate Professor in the computer science department at George Mason University. He directs the Creativity and Graphics Lab (CraGL), whose mission is to solve challenging visual, geometry, and design problems and pursue foundational research into human creativity. His work has been supported by the National Science Foundation (including a CAREER award), Google, and Adobe. His research has been incorporated into Adobe Illustrator as the Puppet Warp tool. Previously he was a post-doctoral researcher in the computer science departments of Columbia University, Rutgers University, Tel-Aviv University, and Herzliya IDC. Yotam earned his Ph.D. in Computer Science from New York University in 2009, where he was awarded the Janet Fabri Prize for most outstanding dissertation.
The talk will be given in hybrid mode, in room 337.