Inverse kinematics for reduced deformable models

Der, Kevin G.; Sumner, Robert W.; Popović, Jovan

doi:10.1145/1141911.1142011

Cited by 86 publications

(13 citation statements)

References 22 publications

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“…The resulting deformation energy function is nonlinear and solved using Gauss-Newton method. The MeshIK method can be accelerated with reduced deformable model in [46]. The reduced deformable model builds on the set of transformations and skinning weights extracted from the example meshes using skinning mesh method [47] .…”

Section: Related Approachesmentioning

confidence: 99%

Gradient Domain Mesh Deformation — A Survey

Zhou

2009

J. Comput. Sci. Technol.

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This survey reviews the recent development of gradient domain mesh deformation method. Different to other deformation methods, the gradient domain deformation method is a surface-based, variational optimization method. It directly encodes the geometric details in differential coordinates, which are also called Laplacian coordinates in literature. By preserving the Laplacian coordinates, the mesh details can be well preserved during deformation. Due to the locality of the Laplacian coordinates, the variational optimization problem can be casted into a sparse linear system. Fast sparse linear solver can be adopted to generate deformation result interactively, or even in real-time. The nonlinear nature of gradient domain mesh deformation leads to the development of two categories of deformation methods: linearization methods and nonlinear optimization methods. Basically, the linearization methods only need to solve the linear least-squares system once. They are fast, easy to understand and control, while the deformation result might be suboptimal. Nonlinear optimization methods can reach optimal solution of deformation energy function by iterative updating. Since the computation of nonlinear methods is expensive, reduced deformable models should be adopted to achieve interactive performance. The nonlinear optimization methods avoid the user burden to input transformation at deformation handles, and they can be extended to incorporate various nonlinear constraints, like volume constraint, skeleton constraint, and so on. We review representative methods and related approaches of each category comparatively and hope to help the user understand the motivation behind the algorithms. Finally, we discuss the relation between physical simulation and gradient domain mesh deformation to reveal why it can achieve physically plausible deformation result.

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Section: Related Approachesmentioning

confidence: 99%

Gradient Domain Mesh Deformation — A Survey

Zhou

2009

J. Comput. Sci. Technol.

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“…Example-based mesh deformation driven by a small number of translational handles instead of a skeleton have been previously investigated in the framework of MeshIK [Sumner et al 2005;Der et al 2006], where deformation gradients are interpolated from example poses and new vertex positions or new bone transformations are solved via a relatively expensive nonlinear optimization, which simultaneously involves all examples. In contrast, this paper adopts a learning approach which has an offline training stage in addition to the run-time animation stage.…”

Section: Related Workmentioning

confidence: 99%

Real-time data driven deformation using kernel canonical correlation analysis

Feng

Kim

2008

ACM Trans. Graph.

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Achieving intuitive control of animated surface deformation while observing a specific style is an important but challenging task in computer graphics. Solutions to this task can find many applications in data-driven skin animation, computer puppetry, and computer games. In this paper, we present an intuitive and powerful animation interface to simultaneously control the deformation of a large number of local regions on a deformable surface with a minimal number of control points. Our method learns suitable deformation subspaces from training examples, and generate new deformations on the fly according to the movements of the control points. Our contributions include a novel deformation regression method based on kernel Canonical Correlation Analysis (CCA) and a Poisson-based translation solving technique for easy and fast deformation control based on examples. Our run-time algorithm can be implemented on GPUs and can achieve a few hundred frames per second even for large datasets with hundreds of training examples.

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“…Utilizing skeletal joints [14], free-form lattices [13] or curves [20] to facilitate driving detailed outmost models is embedded into most commercial packages. A very recent contribution on reduced deformable objects is found in [3], which identifies a small group of proxy vertices that summarize the movement of the example meshes independent of their geometric complexity. Example based Skinning: Example based approaches are often referred as those schemes involving runtime shape interpolations [12] 1-4244-1457-1/08/$25.00 © IEEE computation is required.…”

Section: Related Workmentioning

confidence: 99%

Skinning on Progressive Decimated Models

Xian

Soon

Tian

2008

2008 5th IEEE Consumer Communications and Networking Conference

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Skinning existing animation frames or examples is exploited actively to find the most suitable scheme that is capable of capturing deformations from given mesh sequences. It is necessary to approximate joint transformations by a fitting algorithm that usually involves solving a large scale linear system. In this paper, we reduce the dimensions of the linear system substantially by representing example meshes as progressive decimated models. These models are reconstructed from augmented deformation sensitive decimation (ADSD) that is able to handle large deformations while maintaining connectivity relationship throughout all example meshes. We also show that skinning can be propagated to decimated models, which allows animations to be scalable to specific applications with varied requirements of qualities.

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Inverse kinematics for reduced deformable models

Cited by 86 publications

References 22 publications

Gradient Domain Mesh Deformation — A Survey

Gradient Domain Mesh Deformation — A Survey

Real-time data driven deformation using kernel canonical correlation analysis

Skinning on Progressive Decimated Models

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