Humans intuitively recognize objects' physical properties and predict their motion, even when the objects are engaged in complicated interactions. The abilities to perform physical reasoning and to adapt to new environments, while intrinsic to humans, remain challenging to state-of-the-art computational models. In this work, we present a neural model that simultaneously reasons about physics and make future predictions based on visual and dynamics priors. The visual prior predicts a particle-based representation of the system from visual observations. An inference module operates on those particles, predicting and refining estimates of particle locations, object states, and physical parameters, subject to the constraints imposed by the dynamics prior, which we refer to as visual grounding. We demonstrate the effectiveness of our method in environments involving rigid objects, deformable materials, and fluids. Experiments show that our model can infer the physical properties within a few observations, which allows the model to quickly adapt to unseen scenarios and make accurate predictions into the future.

该论文提出了一种神经模型，同时基于物理学原理和视觉先验进行推理和预测，通过对粒子的估计预测物理参数，即视觉基础，并在刚体、可变形材料和流体的复杂环境中证明了它的有效性，允许模型在几次观察内推断出物理特性，从而快速适应未见过的场景并进行准确的未来预测。

学习物理模型的视觉定位