2000
DOI: 10.1007/978-3-7091-6344-3_10
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Realistic Deformation of Human Body Shapes

Abstract: In this paper we propose a new, generic, multi-layered model for automating the deformations of the skin of human characters based on physiological and anatomical considerations. Muscle motion and deformation is automatically derived from an action line that is deformed using a 1D mass-spring system. We cover the muscle layer with a viscoelastic fat layer that concentrates the crucial dynamics effects of the animation. We present results on a female upper torso.

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Cited by 27 publications
(16 citation statements)
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“…A number of authors used spheres, ellipsoids and other implicit surfaces to detect and resolve collisions for cloth, skin, hair and muscles, see e.g. 48,66,58,4,49,34 . Fisher and Lin 20 used a penalty based formulation to enforce a non-interpenetration constraint between deformable bodies.…”
Section: Related Workmentioning
confidence: 99%
“…A number of authors used spheres, ellipsoids and other implicit surfaces to detect and resolve collisions for cloth, skin, hair and muscles, see e.g. 48,66,58,4,49,34 . Fisher and Lin 20 used a penalty based formulation to enforce a non-interpenetration constraint between deformable bodies.…”
Section: Related Workmentioning
confidence: 99%
“…This can work either with explicit anatomy knowledge [Scheepers et al 1997;Aubel and Thalmann 2000;Teran et al 2005], or without [Capell et al 2002;Guo and Wong 2005;Pratscher et al 2005]. Physically based methods generally obtain a high level of realism (delivering also dynamic effects and muscle bulges), but at high computational costs.…”
Section: Physically Based Methodsmentioning
confidence: 99%
“…Extensive efforts were also put into mapping fibre textures showing orientations for the different muscle classes, providing an ideal visual learning tool. Aubel and Thalman (2000) used a two-layered muscle approach whereby a set of action lines is modelled based on bone movement, and then used to deform an outer surface with muscle collision a feature. This approach was computationally fast and useful in clinical assessment.…”
Section: Background and Previous Workmentioning
confidence: 99%