  Image animation is the task of transferring the motion of a driving video to
a given object in a source image. While great progress has recently been made
in unsupervised motion transfer, requiring no labeled data or domain priors,
many current unsupervised approaches still struggle to capture the motion
deformations when large motion/view discrepancies occur between the source and
driving domains. Under such conditions, there is simply not enough information
to capture the motion field properly. We introduce DiME (Differential Motion
Evolution), an end-to-end unsupervised motion transfer framework integrating
differential refinement for motion estimation. Key findings are twofold: (1) by
capturing the motion transfer with an ordinary differential equation (ODE), it
helps to regularize the motion field, and (2) by utilizing the source image
itself, we are able to inpaint occluded/missing regions arising from large
motion changes. Additionally, we also propose a natural extension to the ODE
idea, which is that DiME can easily leverage multiple different views of the
source object whenever they are available by modeling an ODE per view.
Extensive experiments across 9 benchmarks show DiME outperforms the
state-of-the-arts by a significant margin and generalizes much better to unseen
objects.
