Summary of Latent Intuitive Physics: Learning to Transfer Hidden Physics From a 3d Video, by Xiangming Zhu et al.
Latent Intuitive Physics: Learning to Transfer Hidden Physics from A 3D Video
by Xiangming Zhu, Huayu Deng, Haochen Yuan, Yunbo Wang, Xiaokang Yang
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 The paper introduces latent intuitive physics, a transfer learning framework for simulating fluids from single 3D videos. The approach uses latent features drawn from a learnable prior distribution conditioned on particle states to capture complex physical properties. A parametrized prior learner is trained given visual observations to approximate the inverse graphics’ visual posterior and particle states, which are obtained from a learned neural renderer. The converged prior learner is embedded in a probabilistic physics engine, allowing novel simulations on unseen geometries, boundaries, and dynamics without knowledge of true physical parameters. The model demonstrates strong performance in novel scene simulation, future prediction, and supervised particle simulation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper’s main idea is to create a way for computers to simulate fluids from just looking at videos of the fluid. It uses special features called “latent features” that help capture the hidden properties of fluids. This helps the computer learn how to simulate the fluid in new situations without knowing all the details about the physical world. The approach shows promising results in three areas: simulating new scenes, predicting what will happen next, and controlling particle movements. |
Keywords
» Artificial intelligence » Supervised » Transfer learning
