图像到视频生成模型的外推与解耦：运动建模比你想象的更简单

图像到视频（Image-to-Video，I2V）生成旨在根据给定图像和条件（例如文本）合成视频片段。此任务的关键挑战在于同时生成自然运动，同时保留图像的原始外观。然而，当前的I2V扩散模型（I2V-DMs）常常产生运动程度有限的视频，或 exhibits uncontrollable motion that conflicts with the textual condition. To address these limitations，we propose a novel Extrapolating and Decoupling framework，which introduces model merging techniques to the I2V domain for the first time. Specifically，our framework consists of three separate stages: (1) Starting with a base I2V-DM，we explicitly inject the textual condition into the temporal module using a lightweight，learnable adapter and fine-tune the integrated model to improve motion controllability. (2) We introduce a training-free extrapolation strategy to amplify the dynamic range of the motion，effectively reversing the fine-tuning process to enhance the motion degree significantly. (3) With the above two-stage models excelling in motion controllability and degree，we decouple the relevant parameters associated with each type of motion ability and inject them into the base I2V-DM. Since the I2V-DM handles different levels of motion controllability and dynamics at various denoising time steps，we adjust the motion-aware parameters accordingly over time. Extensive qualitative and quantitative experiments have been conducted to demonstrate the superiority of our framework over existing methods.