  In this work, we introduce FluidsFormer: a transformer-based approach for
fluid interpolation within a continuous-time framework. By combining the
capabilities of PITT and a residual neural network (RNN), we analytically
predict the physical properties of the fluid state. This enables us to
interpolate substep frames between simulated keyframes, enhancing the temporal
smoothness and sharpness of animations. We demonstrate promising results for
smoke interpolation and conduct initial experiments on liquids.
