TRACE: Learning 3D Gaussian Physical Dynamics from Multi-view Videos
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| Format: | Preprint |
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2025
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| _version_ | 1866908487853801472 |
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| author | Li, Jinxi Song, Ziyang Yang, Bo |
| author_facet | Li, Jinxi Song, Ziyang Yang, Bo |
| contents | In this paper, we aim to model 3D scene geometry, appearance, and physical information just from dynamic multi-view videos in the absence of any human labels. By leveraging physics-informed losses as soft constraints or integrating simple physics models into neural nets, existing works often fail to learn complex motion physics, or doing so requires additional labels such as object types or masks. We propose a new framework named TRACE to model the motion physics of complex dynamic 3D scenes. The key novelty of our method is that, by formulating each 3D point as a rigid particle with size and orientation in space, we directly learn a translation rotation dynamics system for each particle, explicitly estimating a complete set of physical parameters to govern the particle's motion over time. Extensive experiments on three existing dynamic datasets and one newly created challenging synthetic datasets demonstrate the extraordinary performance of our method over baselines in the task of future frame extrapolation. A nice property of our framework is that multiple objects or parts can be easily segmented just by clustering the learned physical parameters. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_09811 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TRACE: Learning 3D Gaussian Physical Dynamics from Multi-view Videos Li, Jinxi Song, Ziyang Yang, Bo Computer Vision and Pattern Recognition Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning Robotics In this paper, we aim to model 3D scene geometry, appearance, and physical information just from dynamic multi-view videos in the absence of any human labels. By leveraging physics-informed losses as soft constraints or integrating simple physics models into neural nets, existing works often fail to learn complex motion physics, or doing so requires additional labels such as object types or masks. We propose a new framework named TRACE to model the motion physics of complex dynamic 3D scenes. The key novelty of our method is that, by formulating each 3D point as a rigid particle with size and orientation in space, we directly learn a translation rotation dynamics system for each particle, explicitly estimating a complete set of physical parameters to govern the particle's motion over time. Extensive experiments on three existing dynamic datasets and one newly created challenging synthetic datasets demonstrate the extraordinary performance of our method over baselines in the task of future frame extrapolation. A nice property of our framework is that multiple objects or parts can be easily segmented just by clustering the learned physical parameters. |
| title | TRACE: Learning 3D Gaussian Physical Dynamics from Multi-view Videos |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning Robotics |
| url | https://arxiv.org/abs/2508.09811 |