PhysCtrl: Generative Physics for Controllable and Physics-Grounded Video Generation
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866918191530246144 |
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| author | Wang, Chen Chen, Chuhao Huang, Yiming Dou, Zhiyang Liu, Yuan Gu, Jiatao Liu, Lingjie |
| author_facet | Wang, Chen Chen, Chuhao Huang, Yiming Dou, Zhiyang Liu, Yuan Gu, Jiatao Liu, Lingjie |
| contents | Existing video generation models excel at producing photo-realistic videos from text or images, but often lack physical plausibility and 3D controllability. To overcome these limitations, we introduce PhysCtrl, a novel framework for physics-grounded image-to-video generation with physical parameters and force control. At its core is a generative physics network that learns the distribution of physical dynamics across four materials (elastic, sand, plasticine, and rigid) via a diffusion model conditioned on physics parameters and applied forces. We represent physical dynamics as 3D point trajectories and train on a large-scale synthetic dataset of 550K animations generated by physics simulators. We enhance the diffusion model with a novel spatiotemporal attention block that emulates particle interactions and incorporates physics-based constraints during training to enforce physical plausibility. Experiments show that PhysCtrl generates realistic, physics-grounded motion trajectories which, when used to drive image-to-video models, yield high-fidelity, controllable videos that outperform existing methods in both visual quality and physical plausibility. Project Page: https://cwchenwang.github.io/physctrl |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_20358 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PhysCtrl: Generative Physics for Controllable and Physics-Grounded Video Generation Wang, Chen Chen, Chuhao Huang, Yiming Dou, Zhiyang Liu, Yuan Gu, Jiatao Liu, Lingjie Computer Vision and Pattern Recognition Existing video generation models excel at producing photo-realistic videos from text or images, but often lack physical plausibility and 3D controllability. To overcome these limitations, we introduce PhysCtrl, a novel framework for physics-grounded image-to-video generation with physical parameters and force control. At its core is a generative physics network that learns the distribution of physical dynamics across four materials (elastic, sand, plasticine, and rigid) via a diffusion model conditioned on physics parameters and applied forces. We represent physical dynamics as 3D point trajectories and train on a large-scale synthetic dataset of 550K animations generated by physics simulators. We enhance the diffusion model with a novel spatiotemporal attention block that emulates particle interactions and incorporates physics-based constraints during training to enforce physical plausibility. Experiments show that PhysCtrl generates realistic, physics-grounded motion trajectories which, when used to drive image-to-video models, yield high-fidelity, controllable videos that outperform existing methods in both visual quality and physical plausibility. Project Page: https://cwchenwang.github.io/physctrl |
| title | PhysCtrl: Generative Physics for Controllable and Physics-Grounded Video Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.20358 |