  Formulated as a conditional generation problem, face animation aims at
synthesizing continuous face images from a single source image driven by a set
of conditional face motion. Previous works mainly model the face motion as
conditions with 1D or 2D representation (e.g., action units, emotion codes,
landmark), which often leads to low-quality results in some complicated
scenarios such as continuous generation and largepose transformation. To tackle
this problem, the conditions are supposed to meet two requirements, i.e.,
motion information preserving and geometric continuity. To this end, we propose
a novel representation based on a 3D geometric flow, termed facial flow, to
represent the natural motion of the human face at any pose. Compared with other
previous conditions, the proposed facial flow well controls the continuous
changes to the face. After that, in order to utilize the facial flow for face
editing, we build a synthesis framework generating continuous images with
conditional facial flows. To fully take advantage of the motion information of
facial flows, a hierarchical conditional framework is designed to combine the
extracted multi-scale appearance features from images and motion features from
flows in a hierarchical manner. The framework then decodes multiple fused
features back to images progressively. Experimental results demonstrate the
effectiveness of our method compared to other state-of-the-art methods.
