dence
on real images, which restricts the editability of the synthesized content.
Although some methods incorporate ControlNet to introduce image-based
conditioning, they often lack explicit motion control and require
computationally expensive training. To address these limitations, we propose
AnyI2V, a training-free framework that animates any conditional images with
user-defined motion trajectories. AnyI2V supports a broader range of modalities
as the conditional image, including data types such as meshes