Summary of Attention-based Learning For Fluid State Interpolation and Editing in a Time-continuous Framework, by Bruno Roy
Attention-Based Learning for Fluid State Interpolation and Editing in a Time-Continuous Framework
by Bruno Roy
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Graphics (cs.GR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces FluidsFormer, a transformer-based approach for interpolating fluid dynamics within a continuous-time framework. By combining PITT with a residual neural network (RNN), the model analytically predicts physical properties of the fluid state, enabling interpolation between simulated keyframes and enhancing temporal smoothness and sharpness in animations. The paper demonstrates promising results for smoke interpolation and conducts initial experiments on liquids. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FluidsFormer is a new way to make computer simulations of fluids, like smoke or water, look more realistic. It uses special math formulas to predict what the fluid will do at any point in time, based on some key points that we know it will follow. This makes animations of fluids look smoother and more detailed. The paper shows how well this works for simulating smoke, and also tries it out with liquids. |
Keywords
» Artificial intelligence » Neural network » Rnn » Transformer
