  Face image animation from a single image has achieved remarkable progress.
However, it remains challenging when only sparse landmarks are available as the
driving signal. Given a source face image and a sequence of sparse face
landmarks, our goal is to generate a video of the face imitating the motion of
landmarks. We develop an efficient and effective method for motion transfer
from sparse landmarks to the face image. We then combine global and local
motion estimation in a unified model to faithfully transfer the motion. The
model can learn to segment the moving foreground from the background and
generate not only global motion, such as rotation and translation of the face,
but also subtle local motion such as the gaze change. We further improve face
landmark detection on videos. With temporally better aligned landmark sequences
for training, our method can generate temporally coherent videos with higher
visual quality. Experiments suggest we achieve results comparable to the
state-of-the-art image driven method on the same identity testing and better
results on cross identity testing.
