Attention-Based Learning for Fluid State Interpolation and Editing in a Time-Continuous Framework
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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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 19 references that are in the map. First posted on arXiv 12 Jun 2024.
Builds on
10 of 19 references.
References · 10
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2016Accelerating Eulerian Fluid Simulation With Convolutional Networkscited by 128
- 2018Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flowcited by 97
- 2018tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flowcited by 44
- 2023Physics Informed Token Transformer for Solving Partial Differential Equationscited by 21
- 2017Liquid Splash Modeling with Neural Networkscited by 16
- 2022Continuous-Time Meta-Learning with Forward Mode Differentiationcited by 12
- 2019Dynamic Upsampling of Smoke through Dictionary-based Learningcited by 8
- 2016Interpolations of Smoke and Liquid Simulationscited by 3
- 2021Neural UpFlow: A Scene Flow Learning Approach to Increase the Apparent Resolution of Particle-Based Liquidscited by 2
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Citing papers per year: 2026: 1.
1 paper.