Emotion Editing using Finite Elements

Koch, R; Groß, Markus; Bosshard, Albert A.

doi:10.1111/1467-8659.00276

Cited by 52 publications

(34 citation statements)

References 15 publications

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“…[Borshukov et al 2003]), it is also quite popular elsewhere including applications to lip reading and surgical planning. For example, [Koch et al 1998] pointed out the utility of synthesizing expressions on a post-surgical face to determine the effects of the surgical modifications.…”

Section: Introductionmentioning

confidence: 99%

Automatic determination of facial muscle activations from sparse motion capture marker data

2005

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We built an anatomically accurate model of facial musculature, passive tissue and underlying skeletal structure using volumetric data acquired from a living male subject. The tissues are endowed with a highly nonlinear constitutive model including controllable anisotropic muscle activations based on fiber directions. Detailed models of this sort can be difficult to animate requiring complex coordinated stimulation of the underlying musculature. We propose a solution to this problem automatically determining muscle activations that track a sparse set of surface landmarks, e.g. acquired from motion capture marker data. Since the resulting animation is obtained via a three dimensional nonlinear finite element method, we obtain visually plausible and anatomically correct deformations with spatial and temporal coherence that provides robustness against outliers in the motion capture data. Moreover, the obtained muscle activations can be used in a robust simulation framework including contact and collision of the face with external objects.

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Section: Introductionmentioning

confidence: 99%

Automatic determination of facial muscle activations from sparse motion capture marker data

2005

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“…[2], for instance, develops surface-based snakes to represent human organs. [9] conveys a FEM based model for facial surgery simulation and extend it for animating human emotions [10]. Full volumetric soft tissue models over tetrahedral discretizations can be found in [17].…”

Section: Introductionmentioning

confidence: 99%

Interactive Cuts through 3‐Dimensional Soft Tissue

Bielser

Maiwald

Groß

1999

Computer Graphics Forum

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We describe a physically based framework for real-time modeling and interactive cutting of 3-dimensional soft tissue that can be used for surgery simulation. Unlike existing approaches which are mostly designed for tensorproduct grids our methods operate on tetrahedral decompositions giving more topological and geometric flexibility for the efficient modeling of complex anatomical structures. We start from an initial tetrahedralization such as being provided by any conventional meshing method. In order to track topological changes tetrahedra intersected by the virtual scalpel are split into substructures whose connectivity follows the trajectory of the cut, which can be arbitrary. For the efficient computation of collisions between the scalpel and individual tetrahedra we devised a local collision detection algorithm. The underlying physics is approximated through masses and springs attached to each tetrahedral vertex and edge. A hierarchical Runge-Kutta iteration computes the relaxation of the system by traversing the designed data structures in a breadth-first order. The framework includes a force-feedback interface and uses real-time texture mapping to enhance the visual realism.

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“…The difference between the applied force and the reacting cut friction force results eventually in the acceleration force of the scalpel. (15) In the case of dynamic cut friction all of the three forces described earlier must be considered. Note that and differ from the static case only in terms of smaller coefficients whereas is new.…”

Section: Dynamic Cut-frictionmentioning

confidence: 99%