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
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通过视频扩散模型学习 3D 对象的各种物理属性，设计了一种高度通用的基于粘弹性材料模型的物理仿真系统，通过蒸馏视频扩散模型中的物理先验知识，证明了物理 3D 在虚拟环境中应用真实物理原理的能力。

通过视频扩散学习 3D 高斯函数的物理特性

Physics3D: Learning Physical Properties of 3D Gaussians via Video  Diffusion

Recent years have witnessed the strong power of 3D generation models, which
offer a new level of creative flexibility by allowing users to guide the 3D
content generation process through a single image or natural language. However,
it remains challenging for existing 3D generation methods to create
subject-driven 3D content across diverse prompts. In this paper, we introduce a
novel 3D customization method, dubbed Make-Your-3D that can personalize
high-fidelity and consistent 3D content from only a single image of a subject
with text description within 5 minutes. Our key insight is to harmonize the
distributions of a multi-view diffusion model and an identity-specific 2D
generative model, aligning them with the distribution of the desired 3D
subject. Specifically, we design a co-evolution framework to reduce the
variance of distributions, where each model undergoes a process of learning
from the other through identity-aware optimization and subject-prior
optimization, respectively. Extensive experiments demonstrate that our method
can produce high-quality, consistent, and subject-specific 3D content with
text-driven modifications that are unseen in subject image.

我们引入了一种新颖的 3D 个性化方法，名为 Make-Your-3D，可以从单个主题图像和文本描述中提取个性化的高保真、一致的 3D 内容，在 5 分钟内完成。我们设计了一个共同进化的框架，通过身份感知优化和主题先验优化，使多视图扩散模型和身份特定的 2D 生成模型的分布与期望的 3D 主题的分布相一致。广泛的实验证明我们的方法可以产生高质量、一致性和主题特定的 3D 内容，并进行文本驱动的修改。