  We present a versatile model, FaceAnime, for various video generation tasks
from still images. Video generation from a single face image is an interesting
problem and usually tackled by utilizing Generative Adversarial Networks (GANs)
to integrate information from the input face image and a sequence of sparse
facial landmarks. However, the generated face images usually suffer from
quality loss, image distortion, identity change, and expression mismatching due
to the weak representation capacity of the facial landmarks. In this paper, we
propose to "imagine" a face video from a single face image according to the
reconstructed 3D face dynamics, aiming to generate a realistic and
identity-preserving face video, with precisely predicted pose and facial
expression. The 3D dynamics reveal changes of the facial expression and motion,
and can serve as a strong prior knowledge for guiding highly realistic face
video generation. In particular, we explore face video prediction and exploit a
well-designed 3D dynamic prediction network to predict a 3D dynamic sequence
for a single face image. The 3D dynamics are then further rendered by the
sparse texture mapping algorithm to recover structural details and sparse
textures for generating face frames. Our model is versatile for various AR/VR
and entertainment applications, such as face video retargeting and face video
prediction. Superior experimental results have well demonstrated its
effectiveness in generating high-fidelity, identity-preserving, and visually
pleasant face video clips from a single source face image.
