AnyI2V is a training-free framework that animates conditional images with user-defined motion trajectories, supporting various data types and enabling flexible video generation.
Recent advancements in video generation, particularly in diffusion models,
have driven notable progress in text-to-video (T2V) and image-to-video (I2V)
synthesis. However, challenges remain in effectively integrating dynamic motion
signals and flexible spatial constraints. Existing T2V methods typically rely
on text prompts, which inherently lack precise control over the spatial layout
of generated content. In contrast, I2V methods are limited by their dependence
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 and point clouds
that are not supported by ControlNet, enabling more flexible and versatile
video generation. Additionally, it supports mixed conditional inputs and
enables style transfer and editing via LoRA and text prompts. Extensive
experiments demonstrate that the proposed AnyI2V achieves superior performance
and provides a new perspective in spatial- and motion-controlled video
generation. Code is available at https://henghuiding.com/AnyI2V/.