Fire Visualization Using Eigenfires

Procedural modeling of fire has been very practical and popular, but most of them are based on random parameters for the purpose of creating realistic looking flames, while physical based modeling is closer to the realism, but suffered by complicated algorithms and heavy computational requirement. Our new approach on fire does not use any physical parameters, but uses real-life fire images and applies image-based methods and statistical analysis. We visualize the shape and motion of fire to analyze them, which can be used a simple and realistic fire modeling. We employ principal component analysis (PCA) and take it to a new level by introducing “eigenfires”, which are eigenvectors of the covariance matrix of fire videos, from variety of high-definition videos of real fire to visualize and understand the track of fire movement and how different flames are located in various locations. Our system provides flexibility for the artists to manipulate the ordinary style of a flame and change it to another distinct shape using a series of weights that are assigned to each eigenfire. Our method is also efficient in terms of compact representation of fire motion as PCA allows compression by cutting high dimension data for almost the same quality of the video.