3D Gaussian Splatting has recently enabled fast and photorealistic reconstruction of static 3D scenes. However, dynamic editing of such scenes remains a significant challenge. We introduce a novel framework, Physics-Guided Score Distillation, to address a fundamental conflict: physics simulation provides a strong motion prior that is insufficient for photorealism , while video-based Score Distillation Sampling (SDS) alone cannot generate coherent motion for complex, multi-particle scenarios. We resolve this through a unified optimization framework where physics simulation guides Score Distillation to jointly refine the motion prior for photorealism while simultaneously optimizing appearance. Specifically, we learn a neural dynamics model that predicts particle motion and appearance, optimized end-to-end via a combined loss integrating Video-SDS for photorealism with our physics-guidance prior. This allows for photorealistic refinements while ensuring the dynamics remain plausible. Our framework enables scene-wide dynamic weather effects, including snowfall, rainfall, fog, and sandstorms, with physically plausible motion. Experiments demonstrate our physics-guided approach significantly outperforms baselines, with ablations confirming this joint refinement is essential for generating coherent, high-fidelity dynamics.

翻译：3D高斯泼溅技术近期实现了静态3D场景的快速且照片级逼真的重建。然而，对此类场景的动态编辑仍是一项重大挑战。我们提出了一种新颖框架——物理引导的分数蒸馏，以解决一个根本性矛盾：物理模拟提供了强大的运动先验，但不足以实现照片级逼真效果；而基于视频的分数蒸馏采样（SDS）单独无法为复杂的多粒子场景生成连贯的运动。我们通过一个统一的优化框架解决了这一问题，其中物理模拟引导分数蒸馏共同优化运动先验以实现照片级逼真效果，同时优化外观。具体来说，我们学习了一个预测粒子运动和外观的神经动力学模型，通过结合视频SDS（用于照片级逼真效果）与我们的物理引导先验的联合损失进行端到端优化。这使得在确保动力学合理性的同时实现照片级逼真度的优化。我们的框架能够生成具有物理合理运动的全场景动态天气效果，包括降雪、降雨、雾和沙尘暴。实验表明，我们的物理引导方法显著优于基线方法，消融实验证实这种联合优化对于生成连贯且高保真的动力学至关重要。