  In this paper, we propose CIMS: a novel correction-interpolation method for
smoke simulation. The basis of our method is to first generate a low frame rate
smoke simulation, then increase the frame rate using temporal interpolation.
However, low frame rate smoke simulations are inaccurate as they require
increasing the time-step. A simulation with a larger time-step produces results
different from that of the original simulation with a small time-step.
Therefore, the proposed method corrects the large time-step simulation results
closer to the corresponding small time-step simulation results using a
U-Net-based DNN model. To obtain more precise results, we applied modeling
concepts used in the image domain, such as optical flow and perceptual loss. By
correcting the large time-step simulation results and interpolating between
them, the proposed method can efficiently and accurately generate high frame
rate smoke simulations. We conduct qualitative and quantitative analyses to
confirm the effectiveness of the proposed model. Our analyses show that our
method reduces the mean squared error of large time-step simulation results by
more than 80% on average. Our method also produces results closer to the ground
truth than the previous DNN-based methods; it is on average 2.04 times more
accurate than previous works. In addition, the computation time of the proposed
correction method barely affects the overall computation time.
