# Attention-Based Learning for Fluid State Interpolation and Editing in a Time-Continuous Framework

arXiv: https://arxiv.org/abs/2406.08188 · HTML: https://mlsanity.com/paper/2406.08188/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 12 Jun 2024.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Accelerating Eulerian Fluid Simulation With Convolutional Networks](https://mlsanity.com/paper/1607.03597/) (2016)
- [Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow](https://mlsanity.com/paper/1802.10123/) (2018)
- [tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow](https://mlsanity.com/paper/1801.09710/) (2018)
- [Physics Informed Token Transformer for Solving Partial Differential Equations](https://mlsanity.com/paper/2305.08757/) (2023)
- [Liquid Splash Modeling with Neural Networks](https://mlsanity.com/paper/1704.04456/) (2017)
- [Continuous-Time Meta-Learning with Forward Mode Differentiation](https://mlsanity.com/paper/2203.01443/) (2022)
- [Dynamic Upsampling of Smoke through Dictionary-based Learning](https://mlsanity.com/paper/1910.09166/) (2019)
- [Interpolations of Smoke and Liquid Simulations](https://mlsanity.com/paper/1608.08570/) (2016)
- [Neural UpFlow: A Scene Flow Learning Approach to Increase the Apparent Resolution of Particle-Based Liquids](https://mlsanity.com/paper/2106.05143/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Variational Optimal Transport Operator on Incompressible Flow](https://mlsanity.com/paper/2609.13729/) (2026)

## Abstract (arXiv)

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.