We introduce new approaches of modeling and synthesizing realistic-looking 2D fire animations using video-based techniques and statistical analysis. Our approaches are based on real footage of various small-scale fire samples with customized motions that we captured for this research, and the final results can be utilized as a sequence of images in video games, motion graphics and cinematic visual effects. Instead of conventional physically-based simulation, we utilize example-based principal component analysis (PCA) and take it to a new level by introducing “Eigenfires”, as a new way to represent the main features of various real fire samples. The visualization of Eigenfires helps animators to design the fire interactively through a more meaningful and convenient way in comparison to known procedural approaches or other video-based synthesis models. Our system enables artists to control real-life fire videos through motion transitions and loops by selecting any desired ranges of any video clips and then the system takes care of the remaining part that best represent a smooth transition. Instead of tricking the eyes with a basic blending only between similar shapes, our flexible fire transitions are capable of connecting various fire styles. Our techniques are also effective for data compressions, they can deliver real-time interactive recognition for high resolution images, very easy to implement, and requires little parameter tuning.

We introduce new approaches of modeling and synthesizing realistic-looking 2D fire animations using video-based techniques and statistical analysis. Our approaches are based on real footage of various small-scale fire samples with customized motions that we captured for this research, and the final results can be utilized as a sequence of images in video games, motion graphics and cinematic visual effects. Instead of conventional physically-based simulation, we utilize example-based principal component analysis (PCA) and take it to a new level by introducing “Eigenfires”, as a new way to represent the main features of various real fire samples. The visualization of Eigenfires helps animators to design the fire interactively through a more meaningful and convenient way in comparison to known procedural approaches or other video-based synthesis models. Our system enables artists to control real-life fire videos through motion transitions and loops by selecting any desired ranges of any video clips and then the system takes care of the remaining part that best represent a smooth transition. Instead of tricking the eyes with a basic blending only between similar shapes, our flexible fire transitions are capable of connecting various fire styles. Our techniques are also effective for data compressions, they can deliver real-time interactive recognition for high resolution images, very easy to implement, and requires little parameter tuning.

We introduce new approaches of modeling and synthesizing realistic-looking 2D fire animations using video-based techniques and statistical analysis. Our approaches are based on real footage of various small-scale fire samples with customized motions that we captured for this research, and the final results can be utilized as a sequence of images in video games, motion graphics and cinematic visual effects. Instead of conventional physically-based simulation, we utilize example-based principal component analysis (PCA) and take it to a new level by introducing “Eigenfires”, as a new way to represent the main features of various real fire samples. The visualization of Eigenfires helps animators to design the fire interactively through a more meaningful and convenient way in comparison to known procedural approaches or other video-based synthesis models. Our system enables artists to control real-life fire videos through motion transitions and loops by selecting any desired ranges of any video clips and then the system takes care of the remaining part that best represent a smooth transition. Instead of tricking the eyes with a basic blending only between similar shapes, our flexible fire transitions are capable of connecting various fire styles. Our techniques are also effective for data compressions, they can deliver real-time interactive recognition for high resolution images, very easy to implement, and requires little parameter tuning.