  Motion transfer aims to transfer the motion of a driving video to a source
image. When there are considerable differences between object in the driving
video and that in the source image, traditional single domain motion transfer
approaches often produce notable artifacts; for example, the synthesized image
may fail to preserve the human shape of the source image (cf . Fig. 1 (a)). To
address this issue, in this work, we propose a Motion and Appearance Adaptation
(MAA) approach for cross-domain motion transfer, in which we regularize the
object in the synthesized image to capture the motion of the object in the
driving frame, while still preserving the shape and appearance of the object in
the source image. On one hand, considering the object shapes of the synthesized
image and the driving frame might be different, we design a shape-invariant
motion adaptation module that enforces the consistency of the angles of object
parts in two images to capture the motion information. On the other hand, we
introduce a structure-guided appearance consistency module designed to
regularize the similarity between the corresponding patches of the synthesized
image and the source image without affecting the learned motion in the
synthesized image. Our proposed MAA model can be trained in an end-to-end
manner with a cyclic reconstruction loss, and ultimately produces a
satisfactory motion transfer result (cf . Fig. 1 (b)). We conduct extensive
experiments on human dancing dataset Mixamo-Video to Fashion-Video and human
face dataset Vox-Celeb to Cufs; on both of these, our MAA model outperforms
existing methods both quantitatively and qualitatively.
