In recent years, there has been rapid development in 3D generation models, opening up new possibilities for applications such as simulating the dynamic movements of 3D objects and customizing their behaviors. However, current 3D generative models tend to focus only on surface features such as color and shape, neglecting the inherent physical properties that govern the behavior of objects in the real world. To accurately simulate physics-aligned dynamics, it is essential to predict the physical properties of materials and incorporate them into the behavior prediction process. Nonetheless, predicting the diverse materials of real-world objects is still challenging due to the complex nature of their physical attributes. In this paper, we propose \textbf{Physics3D}, a novel method for learning various physical properties of 3D objects through a video diffusion model. Our approach involves designing a highly generalizable physical simulation system based on a viscoelastic material model, which enables us to simulate a wide range of materials with high-fidelity capabilities. Moreover, we distill the physical priors from a video diffusion model that contains more understanding of realistic object materials. Extensive experiments demonstrate the effectiveness of our method with both elastic and plastic materials. Physics3D shows great potential for bridging the gap between the physical world and virtual neural space, providing a better integration and application of realistic physical principles in virtual environments. Project page: https://liuff19.github.io/Physics3D.

翻译：近年来，三维生成模型发展迅速，为模拟三维物体的动态运动及定制其行为等应用开辟了新可能性。然而，当前的三维生成模型往往仅关注颜色与形状等表面特征，忽略了现实世界中支配物体行为的内在物理属性。为精确模拟符合物理规律的动态过程，必须预测材料的物理属性并将其纳入行为预测流程。尽管如此，由于真实物体物理属性的复杂性，预测其多样化的材料特性仍具挑战性。本文提出 \textbf{Physics3D}，一种通过视频扩散模型学习三维物体多种物理属性的新方法。我们的方案基于粘弹性材料模型设计了一个高度可泛化的物理仿真系统，使其能够以高保真能力模拟多种材料。此外，我们从包含对真实物体材料更深入理解的视频扩散模型中提炼物理先验知识。大量实验证明了我们的方法在弹性与塑性材料上的有效性。Physics3D 在弥合物理世界与虚拟神经空间之间的鸿沟方面展现出巨大潜力，为在虚拟环境中更完善地融入与应用真实物理原理提供了可能。项目页面：https://liuff19.github.io/Physics3D。