Generating videos with realistic and physically plausible motion is one of the main recent challenges in computer vision. While diffusion models are achieving compelling results in image generation, video diffusion models are limited by heavy training and huge models, resulting in videos that are still biased to the training dataset. In this work we propose MotionCraft, a new zero-shot video generator to craft physics-based and realistic videos. MotionCraft is able to warp the noise latent space of an image diffusion model, such as Stable Diffusion, by applying an optical flow derived from a physics simulation. We show that warping the noise latent space results in coherent application of the desired motion while allowing the model to generate missing elements consistent with the scene evolution, which would otherwise result in artefacts or missing content if the flow was applied in the pixel space. We compare our method with the state-of-the-art Text2Video-Zero reporting qualitative and quantitative improvements, demonstrating the effectiveness of our approach to generate videos with finely-prescribed complex motion dynamics. Project page: https://mezzelfo.github.io/MotionCraft/

提出了一种新的零样本视频生成器MotionCraft，通过应用来自物理模拟的光流对图像扩散模型（如稳定扩散）的噪声潜空间进行变形，从而实现基于物理的和逼真的视频生成，以生成具有复杂运动动力学的视频。与现有方法相比，实现了定性和定量改进，证明了该方法的有效性。

MotionCraft: 基于物理的零样本视频生成