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.

翻译：本文提出FluidsFormer：一种基于Transformer的连续时间框架流体插值方法。该方法结合PITT与残差神经网络（RNN）的建模能力，通过解析方式预测流体状态的物理属性。这使我们能够在模拟关键帧之间插值生成子步长帧，从而提升动画的时间平滑度与视觉锐度。我们在烟雾插值任务中展示了优异效果，并针对液体场景开展了初步实验。