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Conference Paper

Learning Flames


Stich,  Timo
Graphics - Optics - Vision, MPI for Informatics, Max Planck Society;


Magnor,  Marcus
Graphics - Optics - Vision, MPI for Informatics, Max Planck Society;

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Stich, T., & Magnor, M. (2005). Learning Flames. In G. Greiner, J. Hornegger, H. Niemann, & M. Stamminger (Eds.), Vision, Modeling, and Visualization 2005 (pp. 65-70). Berlin: Akademische Verlagsgesellschaft Aka.

Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-287B-7
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.