Enhancing a still image with motion offers more engaged visual experience.
Traditional image animation techniques mainly focus on animating natural scenes
with random dynamics, such as clouds and fluid, and thus limits their
applicability to generic visual contents. To overcome this limitation, we
explore the synthesis of dynamic content for open-domain images, converting
them into animated videos. The key idea is to utilize the motion prior of
text-to-video diffusion models by incorporating the image into the generative
process as guidance. Given an image, we first project it into a text-aligned
rich image embedding space using a learnable image encoding network, which
facilitates the video model to digest the image content compatibly. However,
some visual details still struggle to be preserved in the resulting videos. To
supplement more precise image information, we further feed the full image to
the diffusion model by concatenating it with the initial noises. Experimental
results reveal that our proposed method produces visually convincing animated
videos, exhibiting both natural motions and high fidelity to the input image.
Comparative evaluation demonstrates the notable superiority of our approach
over existing competitors. The source code will be released upon publication.

通过将静止图像与动态相结合，提供更具吸引力的视觉体验。