  One-shot talking head video generation uses a source image and driving video
to create a synthetic video where the source person's facial movements imitate
those of the driving video. However, differences in scale between the source
and driving images remain a challenge for face reenactment. Existing methods
attempt to locate a frame in the driving video that aligns best with the source
image, but imprecise alignment can result in suboptimal outcomes.
  To this end, we introduce a scale transformation module that can
automatically adjust the scale of the driving image to fit that of the source
image, by using the information of scale difference maintained in the detected
keypoints of the source image and the driving frame. Furthermore, to keep
perceiving the scale information of faces during the generation process, we
incorporate the scale information learned from the scale transformation module
into each layer of the generation process to produce a final result with an
accurate scale. Our method can perform accurate motion transfer between the two
images without any anchor frame, achieved through the contributions of the
proposed online scale transformation facial reenactment network. Extensive
experiments have demonstrated that our proposed method adjusts the scale of the
driving face automatically according to the source face, and generates
high-quality faces with an accurate scale in the cross-identity facial
reenactment.
