Summary of Physics3d: Learning Physical Properties Of 3d Gaussians Via Video Diffusion, by Fangfu Liu et al.
Physics3D: Learning Physical Properties of 3D Gaussians via Video Diffusion
by Fangfu Liu, Hanyang Wang, Shunyu Yao, Shengjun Zhang, Jie Zhou, Yueqi Duan
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Physics3D method is a novel approach for learning various physical properties of 3D objects through a video diffusion model. The current 3D generative models focus on surface features, neglecting the inherent physical properties that govern object behavior in the real world. To accurately simulate physics-aligned dynamics, it’s essential to predict and incorporate physical properties into the behavior prediction process. Physics3D involves designing a highly generalizable physical simulation system based on a viscoelastic material model, enabling the simulation of various materials with high-fidelity capabilities. The method distills physical priors from a video diffusion model containing realistic object materials. Extensive experiments demonstrate the effectiveness of Physics3D for both elastic and plastic materials. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Physics3D is a new way to teach computers about the physical properties of 3D objects. Right now, computers are really good at making fake objects look like real ones, but they don’t understand how those objects behave in the real world. This paper proposes a new method that helps computers learn about different materials and how they react to things like pushes and pulls. The authors use a special kind of computer model called a video diffusion model to teach the computer about all sorts of materials. They show that their method works well for both stretchy and breakable materials. |
Keywords
» Artificial intelligence » Diffusion model
