  Given a source image and a driving video depicting the same object type, the
motion transfer task aims to generate a video by learning the motion from the
driving video while preserving the appearance from the source image. In this
paper, we propose a novel structure-aware motion modeling approach, the
deformable anchor model (DAM), which can automatically discover the motion
structure of arbitrary objects without leveraging their prior structure
information. Specifically, inspired by the known deformable part model (DPM),
our DAM introduces two types of anchors or keypoints: i) a number of motion
anchors that capture both appearance and motion information from the source
image and driving video; ii) a latent root anchor, which is linked to the
motion anchors to facilitate better learning of the representations of the
object structure information. Moreover, DAM can be further extended to a
hierarchical version through the introduction of additional latent anchors to
model more complicated structures. By regularizing motion anchors with latent
anchor(s), DAM enforces the correspondences between them to ensure the
structural information is well captured and preserved. Moreover, DAM can be
learned effectively in an unsupervised manner. We validate our proposed DAM for
motion transfer on different benchmark datasets. Extensive experiments clearly
demonstrate that DAM achieves superior performance relative to existing
state-of-the-art methods.
