Chenguo Lin (@lin_chenguo)
2025-02-03 | โค๏ธ 26 | ๐ 2
๐จICLR๐จ
Thank @janusch_patas for featuring our work! Project with @yuchenlin0612 ๐ซก Congratulations ๐ฅณ
Constitutive attributes of 3DGS for various physical phenomena are optimized from a text-to-video model.
2025 is the year of physics-based AI!
Code: https://github.com/wgsxm/OmniPhysGS
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MrNeRF (@janusch_patas)
OmniPhysGS: 3D Constitutive Gaussians for General Physics-Based Dynamics Generation
Abstract (excerpt): We propose OmniPhysGS for synthesizing a physics-based 3D dynamic scene composed of more general objects.
A key design of OmniPhysGS is treating each 3D asset as a collection of constitutive 3D Gaussians. For each Gaussian, its physical material is represented by an ensemble of 12 physical domain-expert sub-models (rubber, metal, honey, water, etc.), greatly enhancing the flexibility of the proposed model.
In the implementation, we define a scene by user-specified prompts and supervise the estimation of material weighting factors via a pretrained video diffusion model. Comprehensive experiments demonstrate that OmniPhysGS achieves more general and realistic physical dynamics across a broader spectrum of materials, including elastic, viscoelastic, plastic, and fluid substances, as well as interactions between different materials.
Our method surpasses existing methods by approximately 3% to 16% in metrics of visual quality and text alignment.
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Tags
domain-vision-3d domain-simulation domain-dev-tools domain-visionos