  We propose novel motion representations for animating articulated objects
consisting of distinct parts. In a completely unsupervised manner, our method
identifies object parts, tracks them in a driving video, and infers their
motions by considering their principal axes. In contrast to the previous
keypoint-based works, our method extracts meaningful and consistent regions,
describing locations, shape, and pose. The regions correspond to semantically
relevant and distinct object parts, that are more easily detected in frames of
the driving video. To force decoupling of foreground from background, we model
non-object related global motion with an additional affine transformation. To
facilitate animation and prevent the leakage of the shape of the driving
object, we disentangle shape and pose of objects in the region space. Our model
can animate a variety of objects, surpassing previous methods by a large margin
on existing benchmarks. We present a challenging new benchmark with
high-resolution videos and show that the improvement is particularly pronounced
when articulated objects are considered, reaching 96.6% user preference vs. the
state of the art.
