Physics3D: Learning Physical Properties of 3D Gaussians via Video
  Diffusion
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.
666a7efa3263a8feca5e4d91
Fangfu Liu
claimed_verified
2024-06-13T09:30:23.756Z
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/avatars/bbf781594fc8c812316711aa8e2797aa.svg
Fangfu Liu
user
Liuff23
666a7efa3263a8feca5e4d92
Hanyang Wang
admin_assigned
2024-06-13T09:47:49.950Z
65c38f6c137aba2aee524989
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Hanyang Wang
user
hanyang-21
666a7efa3263a8feca5e4d93
Shunyu Yao
admin_assigned
2024-06-13T09:48:15.259Z
62aa3b8f46d66bafe2f55899
/avatars/d45c6406042c3f6167153aba76d03776.svg
Shunyu Yao
user
ysymyth
666a7efa3263a8feca5e4d94
Shengjun Zhang
admin_assigned
2024-06-13T09:48:21.610Z
65fd0dfdfdc5e8ee7d15f793
/avatars/dc1073c79dfa5f4d683fc9cea46db645.svg
Shengjun Zhang
user
zhangsj0722
666a7efa3263a8feca5e4d95
Jie Zhou
666a7efa3263a8feca5e4d96
Yueqi Duan
Physics3D: Learning Physical Properties of 3D Gaussians via Video
  Diffusion
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.
Physics3D uses a video diffusion model to learn and simulate the physical properties of 3D objects, enabling realistic behavior prediction in virtual environments.
3D generation models dynamic movements physical properties video diffusion model viscoelastic material model physical priors elastic materials plastic materials