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184 | 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. 185 |
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226 | @inproceedings{physctrl2025,
227 | Author = {Chen Wang* and Chuhao Chen* and Yiming Huang and Zhiyang Dou and Yuan Liu and Jiatao Gu and Lingjie Liu},
228 | Title = {PhysCtrl: Generative Physics for Controllable and Physics-Grounded Video Generation},
229 | Year = {2025},
230 | booktitle={NeurIPS},
231 | }
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