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Conference Paper

Dynamic Surface Reconstruction from 4D-MR Images


Seidel,  Hans-Peter
Computer Graphics, MPI for Informatics, Max Planck Society;

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Fenchel, M., Gumhold, S., & Seidel, H.-P. (2005). Dynamic Surface Reconstruction from 4D-MR Images. In G. Greiner, J. Hornegger, H. Niemann, & M. Stamminger (Eds.), Vision, Modeling, and Visualization 2005 (pp. 249-256). Berlin: Akademische Verlagsgesellschaft Aka.

Cite as: http://hdl.handle.net/11858/00-001M-0000-0023-E6EB-F
In this work we propose a novel approach for realistic fire animation and manipulation. We apply a statistical learning method to an image sequence of a real-world flame to jointly capture flame motion and appearance characteristics. A low-dimensional generic flame model is then robustly matched to the video images. The model parameter values are used as input to drive an Expectation-Maximization algorithm to learn an {\em auto regressive process} with respect to flame dynamics. The generic flame model and the trained motion model enable us to synthesize new, unique flame sequences of arbitrary length in real-time.