Dynamic Real-Time Deformations using Space and Time Adaptive Sampling

Gilles Debunne 1 Mathieu Desbrun 2 Marie-Paule Cani 1, * Alan H. Barr
* Corresponding author
1 iMAGIS - Models, Algorithms and Geometry for Computer Generated Image Graphics
GRAVIR - IMAG - Graphisme, Vision et Robotique, Inria Grenoble - Rhône-Alpes
Abstract : This paper presents the first robust method for animating dynamic visco-elastic deformable objects that provides a guaranteed frame rate. The approach uses an automatic space and time adaptive level of detail technique, in combination with a large-displacement (Green) strain tensor formulation. The body is hierarchically partitioned into a number of tetrahedral regions and mass samples. The local resolution is determined by a quality condition that indicates where and when the resolution is too coarse. As the object moves and deforms, the sampling is refined to concentrate the computational load into the regions that deform the most. Our model consist of a continuous equation solved using a local explicit finite element method. We demonstrate that our adaptive Green strain tensor formulation virtually suppresses unwanted artifacts in the dynamic behavior, compared to adaptive mass-spring and other adaptive approaches. In particular, damped elastic vibration modes are shown to be nearly unchanged for several levels of refinement. Results are presented in the context of a virtual reality system. The user interacts in real-time with the dynamic object (such as a liver) through the control of a rigid tool, attached to a haptic device driven with forces derived from the method.
Mots-clés : animation multiresolution
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Conference papers
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Gilles Debunne, Mathieu Desbrun, Marie-Paule Cani, Alan H. Barr. Dynamic Real-Time Deformations using Space and Time Adaptive Sampling. Computer Graphics Proceedings, 2001, Los Angeles, California, United States. ⟨inria-00510045⟩

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