  Image animation brings life to the static object in the source image
according to the driving video. Recent works attempt to perform motion transfer
on arbitrary objects through unsupervised methods without using a priori
knowledge. However, it remains a significant challenge for current unsupervised
methods when there is a large pose gap between the objects in the source and
driving images. In this paper, a new end-to-end unsupervised motion transfer
framework is proposed to overcome such issue. Firstly, we propose thin-plate
spline motion estimation to produce a more flexible optical flow, which warps
the feature maps of the source image to the feature domain of the driving
image. Secondly, in order to restore the missing regions more realistically, we
leverage multi-resolution occlusion masks to achieve more effective feature
fusion. Finally, additional auxiliary loss functions are designed to ensure
that there is a clear division of labor in the network modules, encouraging the
network to generate high-quality images. Our method can animate a variety of
objects, including talking faces, human bodies, and pixel animations.
Experiments demonstrate that our method performs better on most benchmarks than
the state of the art with visible improvements in pose-related metrics.
