Procedural Animation of Aurora and its Optimization for Keyframe Animation

There have been many studies regarding visual simulations that consider the characteristic movement of auroras. We have proposed a method of generating animation of auroras in the desired form and in the desired location visualized by the users. This study is based on the method proposed by Kojima et. al [5], in which shape control is performed comparatively easily through parameter adjustment. With this method, an artificial 2D distributed simulation of auroras, comprised of inflow points for charged particles flowing from space, has been produced. The curtain-shaped movement of auroras can be reproduced by applying a kinetic model using an electromagnetic field calculation and a fluid calculation within the simulation space. We can see that the reproduction of aurora-specific movement is dependent on the initial value of the current volume flowing from the various flow points. In this way, we attempted to control the shape of the desired aurora by controlling the current flow.

In this study, we extracted two frames from the live-captured aurora video, and, set the initial distribution and target distribution of the aurora by reproducing the respective aurora distributions in 3D. As the respective distributions feature flow limits of charged particles forming an aurora 100 km above the ground, and many aurora video images often capture the horizon, we set the camera position as the point of origin and calculated the world coordinates for the lowest section of the aurora. A genetic algorithm was used to optimize the current flows. We set the cost function as the difference between the electric potential of the target shape and the electric potential based on the simulation results for the coordinates of each flow point. In addition, the number of searched parameters were reduced, assuming that the current distribution flowing to each flow point changes along with the initial shape functionally by expanding this function in a Fourier series, General shape control made possible through optimization. In the future works, we aim to increase control accuracy and gain the ability to control complex shapes.