Physics3D: Learning Physical Properties of 3D Gaussians via Video Diffusion
Fangfu Liu, Hanyang Wang, Shunyu Yao, Shengjun Zhang, Jie Zhou, Yueqi Duan
2024-06-13
Summary
This paper introduces Physics3D, a new method for understanding the physical properties of 3D objects by using a video diffusion model. It aims to improve how we simulate and interact with 3D objects in a realistic way.
What's the problem?
Many existing 3D generation models focus mainly on visual aspects like color and shape, but they often ignore the physical properties that affect how these objects behave in real life. Accurately simulating how materials react under different conditions is challenging because real-world materials have complex characteristics that are hard to predict.
What's the solution?
The authors developed Physics3D, which uses a viscoelastic material model to simulate a variety of materials realistically. This method allows for detailed simulations of both elastic (bouncy) and plastic (moldable) behaviors. They also use a video diffusion model to learn about these physical properties from real-world videos, enabling better predictions of how 3D objects should move and interact. Through extensive testing, they showed that Physics3D can effectively simulate realistic dynamics across different materials.
Why it matters?
This research is important because it enhances our ability to create realistic simulations of 3D objects, which can be applied in fields like gaming, virtual reality, and robotics. By bridging the gap between the physical properties of materials and their virtual representations, Physics3D can lead to more immersive and accurate experiences in digital environments.
Abstract
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 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.