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.
近年来，3D生成模型的发展迅速，为模拟3D对象的动态运动和自定义其行为等应用开辟了新的可能性。然而，当前的3D生成模型倾向于只关注表面特征，如颜色和形状，忽视了控制现实世界中物体行为的固有物理属性。为了准确模拟符合物理的动态，预测材料的物理属性并将其纳入行为预测过程是至关重要的。尽管如此，由于物理属性的复杂性，预测真实世界物体的多样材料仍然具有挑战性。在这篇论文中，我们提出了一种名为 Physics3D 的新方法，通过视频扩散模型学习3D对象的各种物理属性。我们的方法涉及设计一个基于粘弹性材料模型的高度泛化的物理仿真系统，该系统能够高保真地模拟各种材料。此外，我们从包含对现实物体材料更深理解的视频扩散模型中提取物理先验。广泛的实验表明，我们的方法在弹性和塑性材料上都有效。Physics3D显示出极大的潜力，能够桥接物理世界和虚拟神经空间之间的差距，为虚拟环境中现实物理原理的更好集成和应用提供支持。