title: Physics3D: Learning Physical Properties of 3D Gaussians via Video
    Diffusion
summary: 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.
paper_authors._id: 666a7efa3263a8feca5e4d91
paper_authors.name: Fangfu Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-13T09:30:23.756Z
paper_authors._id: 6505a02f9310ce8c400edc63
paper_authors.avatarUrl: /avatars/bbf781594fc8c812316711aa8e2797aa.svg
paper_authors.fullname: Fangfu Liu
paper_authors.type: user
paper_authors.user: Liuff23
paper_authors._id: 666a7efa3263a8feca5e4d92
paper_authors.name: Hanyang Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-13T09:47:49.950Z
paper_authors._id: 65c38f6c137aba2aee524989
paper_authors.avatarUrl: /avatars/a93e29f55876df3e65e2532972e057e4.svg
paper_authors.fullname: Hanyang Wang
paper_authors.type: user
paper_authors.user: hanyang-21
paper_authors._id: 666a7efa3263a8feca5e4d93
paper_authors.name: Shunyu Yao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-13T09:48:15.259Z
paper_authors._id: 62aa3b8f46d66bafe2f55899
paper_authors.avatarUrl: /avatars/d45c6406042c3f6167153aba76d03776.svg
paper_authors.fullname: Shunyu Yao
paper_authors.type: user
paper_authors.user: ysymyth
paper_authors._id: 666a7efa3263a8feca5e4d94
paper_authors.name: Shengjun Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-13T09:48:21.610Z
paper_authors._id: 65fd0dfdfdc5e8ee7d15f793
paper_authors.avatarUrl: /avatars/dc1073c79dfa5f4d683fc9cea46db645.svg
paper_authors.fullname: Shengjun Zhang
paper_authors.type: user
paper_authors.user: zhangsj0722
paper_authors._id: 666a7efa3263a8feca5e4d95
paper_authors.name: Jie Zhou
paper_authors._id: 666a7efa3263a8feca5e4d96
paper_authors.name: Yueqi Duan
paper_title: Physics3D: Learning Physical Properties of 3D Gaussians via Video
    Diffusion
paper_summary: 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.
paper_ai_keywords: video diffusion model viscoelastic material model physical simulation system physical priors