Humans have a remarkable capacity to understand the physical dynamics of
objects in their environment, flexibly capturing complex structures and
interactions at multiple levels of detail. Inspired by this ability, we propose
a hierarchical particle-based object representation that covers a wide variety
of types of three-dimensional objects, including both arbitrary rigid
geometrical shapes and deformable materials. We then describe the Hierarchical
Relation Network (HRN), an end-to-end differentiable neural network based on
hierarchical graph convolution, that learns to predict physical dynamics in
this representation. Compared to other neural network baselines, the HRN
accurately handles complex collisions and nonrigid deformations, generating
plausible dynamics predictions at long time scales in novel settings, and
scaling to large scene configurations. These results demonstrate an
architecture with the potential to form the basis of next-generation physics
predictors for use in computer vision, robotics, and quantitative cognitive
science.

提出了一种分层粒子物体表示和基于分层图卷积的神经网络 HRN，可以准确预测和生成复杂碰撞和非刚性变形的物理动力学，具有潜在的在计算机视觉、机器人和计算认知科学领域中使用的下一代物理学预测模型的潜力。