  Face animation aims at creating photo-realistic portrait videos with animated
poses and expressions. A common practice is to generate displacement fields
that are used to warp pixels and features from source to target. However, prior
attempts often produce sub-optimal displacements. In this work, we present a
geometry driven model and propose two geometric patterns as guidance: 3D face
rendered displacement maps and posed neural codes. The model can optionally use
one of the patterns as guidance for displacement estimation. To model
displacements at locations not covered by the face model (e.g., hair), we
resort to source image features for contextual information and propose a
progressive warping module that alternates between feature warping and
displacement estimation at increasing resolutions. We show that the proposed
model can synthesize portrait videos with high fidelity and achieve the new
state-of-the-art results on the VoxCeleb1 and VoxCeleb2 datasets for both cross
identity and same identity reconstruction.
