Cited By
View all- Marquis Bolduc MPhan H(2022)Rig Inversion by Training a Differentiable Rig FunctionSIGGRAPH Asia 2022 Technical Communications10.1145/3550340.3564218(1-4)Online publication date: 6-Dec-2022
Analytically Learning an Inverse Rig Mapping
Article No.: 27, Pages 1 - 2
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
[1]
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
[2]
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
[3]
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
[4]
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
[5]
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/10.1177/027836498400300404 arXiv:https://doi.org/10.1177/027836498400300404
- Analytically Learning an Inverse Rig Mapping
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Published In
August 2020
152 pages
ISBN:9781450379717
DOI:10.1145/3388767
Copyright © 2020 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Published: 20 August 2020
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View all- Marquis Bolduc MPhan H(2022)Rig Inversion by Training a Differentiable Rig FunctionSIGGRAPH Asia 2022 Technical Communications10.1145/3550340.3564218(1-4)Online publication date: 6-Dec-2022
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