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Linear combination of transformations
Abstract
Geometric transformations are most commonly represented as square matrices in computer graphics. Following simple geometric arguments we derive a natural and geometrically meaningful definition of scalar multiples and a commutative addition of transformations based on the matrix representation, given that the matrices have no negative real eigenvalues. Together, these operations allow the linear combination of transformations. This provides the ability to create weighted combination of transformations, interpolate between transformations, and to construct or use arbitrary transformations in a structure similar to a basis of a vector space. These basic techniques are useful for synthesis and analysis of motions or animations. Animations through a set of key transformations are generated using standard techniques such as subdivision curves. For analysis and progressive compression a PCA can be applied to sequences of transformations. We describe an implementation of the techniques that enables an easy-to-use and transparent way of dealing with geometric transformations in graphics software. We compare and relate our approach to other techniques such as matrix decomposition and quaternion interpolation.
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Cited by
Bai, W., Shi, W., de Marvao, A., Dawes, T. J. W., O’Regan, D. P., Cook, S. A., & Rueckert, D. (2015) · Medical Image Analysis · doi:10.1016/j.media.2015.08.009 · Cited by 171
Animation space: A truly linear framework for character animation
Merry, B., Marais, P., & Gain, J. (2006) · ACM Transactions on Graphics · doi:10.1145/1183287.1183294 · Cited by 91
Metadata
- Authors
- Alexa, M.
- Venue
- Proceedings of the 29th annual conference on Computer graphics and interactive techniques
- Publisher
- ACM
- Published
- 2002-07
- Pages
- 380-387
- OpenAlex
- W2245379371
- Cited by
- 184
- References
- 0
- FWCI
- 6.34
- Type
- conference paper
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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