“Analytically Learning an Inverse Rig Mapping” by Gustafson, Lo and Kanyuk
Conference:
Type(s):
Entry Number: 27
Title:
- Analytically Learning an Inverse Rig Mapping
Presenter(s)/Author(s):
Abstract:
One of the main obstacles to applying the latest advances in motion synthesis to feature film character animation is that these methods operate directly on skeletons instead of high-level rig parameters. Loosely known as the “rig inversion problem,” this hurdle has prevented the crowd department at Pixar from procedurally modifying character skeletons close to camera, knowing that these procedural edits would not be reliably transferred to the equivalent motion on the full character for polish.
Prior attempts at solving this problem have tended to involve hard-coded heuristics, which are cumbersome for production to debug and maintain. To alleviate this overhead, we have adopted an approach of solving the inversion problem with an iterative least-squares algorithm. However, although there are numerous existing methods for solving this problem, the computation of the rig Jacobian is a frequent requirement, which in practice is prohibitively expensive. To accelerate this process, we propose a method wherein an approximation of the rig is derived analytically, through an offline learning process. Using this approximation, we invert full character rigs at real-time rates.
References:
Fabian Hahn, Sebastian Martin, Bernhard Thomaszewski, Robert Sumner, Stelian Coros, and Markus Gross. 2012. Rig-Space Physics. ACM Trans. Graph. 31, 4, Article 72 (July 2012), 8 pages. https://doi.org/10.1145/2185520.2185568
Daniel Holden, Jun Saito, and Taku Komura. 2015. Learning an Inverse Rig Mapping for Character Animation. In Proceedings of the 14th ACM SIGGRAPH / Eurographics Symposium on Computer Animation (Los Angeles, California) (SCA ’15). Association for Computing Machinery, New York, NY, USA, 165–173. https://doi.org/10.1145/ 2786784.2786788
Daniel Holden, Jun Saito, and Taku Komura. 2017. Learning Inverse Rig Mappings by Nonlinear Regression. IEEE Transactions on Visualization and Computer Graphics 23, 3 (March 2017), 1167–1178. https://doi.org/10.1109/TVCG.2016.2628036
Kenneth Levenberg. 1944. A method for the solution of certain non-linear problems in least squares. Quart. Appl. Math. 2, 2 (July 1944), 164–168. https://doi.org/10.1090/qam/10666
David E. Orin and William W. Schrader. 1984. Efficient Computation of the Jacobian for Robot Manipulators. The International Journal of Robotics Research 3, 4 (1984), 66–75. https://doi.org/10.1177/027836498400300404arXiv:https://doi.org/10.1177/027836498400300404
