We introduce latent intuitive physics, a transfer learning framework for physics simulation that can infer hidden properties of fluids from a single 3D video and simulate the observed fluid in novel scenes. Our key insight is to use latent features drawn from a learnable prior distribution conditioned on the underlying particle states to capture the invisible and complex physical properties. To achieve this, we train a parametrized prior learner given visual observations to approximate the visual posterior of inverse graphics, and both the particle states and the visual posterior are obtained from a learned neural renderer. The converged prior learner is embedded in our probabilistic physics engine, allowing us to perform novel simulations on unseen geometries, boundaries, and dynamics without knowledge of the true physical parameters. We validate our model in three ways: (i) novel scene simulation with the learned visual-world physics, (ii) future prediction of the observed fluid dynamics, and (iii) supervised particle simulation. Our model demonstrates strong performance in all three tasks.

翻译：我们提出了潜在直觉物理，这是一种用于物理模拟的迁移学习框架，能够从单个三维视频推断流体的隐藏属性，并在新场景中模拟所观测到的流体。我们的核心见解是利用从可学习先验分布中提取的潜在特征来捕捉不可见且复杂的物理属性，该先验分布以底层粒子状态为条件。为实现这一目标，我们训练一个参数化的先验学习器，在给定视觉观测的情况下近似逆图形的视觉后验，而粒子状态和视觉后验均通过一个已学习的神经渲染器获得。收敛后的先验学习器被嵌入到我们的概率物理引擎中，使我们能够在未知几何、边界和动力学条件下执行新模拟，而无需了解真实的物理参数。我们从三个方面验证了模型性能：(i) 利用习得的视觉世界物理进行新场景模拟，(ii) 对观测流体动力学的未来预测，以及(iii) 有监督的粒子模拟。我们的模型在所有三项任务中均展现出卓越的性能。