Physics-Informed Video Generation via Mixture-of-Experts Latent Alignment
Large-scale video generation models have made remarkable progress in semantic consistency and visual quality, producing videos that are increasingly coherent and visually convincing. Nevertheless, the dynamics induced by pixel-level fitting do not naturally accommodate the regularities that govern real-world motion and interaction, resulting in persistent shortcomings in physical plausibility. To address this limitation, we propose \textbf{PILA} (Physics-Informed Latent Alignment), a framework that injects physics-structured latent guidance into the frozen flow-matching dynamics of pretrained video models. Specifically, PILA first employs anchored field estimation to map frozen-generator ...
| Authors | Cong Wang, Hanxin Zhu, Jiayi Luo et al. |
|---|---|
| Primary category | cs.CV |
| Published | 3 June 2026 |
| Last updated at source | 3 June 2026 |
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