  Talking face synthesis has been widely studied in either appearance-based or
warping-based methods. Previous works mostly utilize single face image as a
source, and generate novel facial animations by merging other person's facial
features. However, some facial regions like eyes or teeth, which may be hidden
in the source image, can not be synthesized faithfully and stably. In this
paper, We present a landmark driven two-stream network to generate faithful
talking facial animation, in which more facial details are created, preserved
and transferred from multiple source images instead of a single one.
Specifically, we propose a network consisting of a learning and fetching
stream. The fetching sub-net directly learns to attentively warp and merge
facial regions from five source images of distinctive landmarks, while the
learning pipeline renders facial organs from the training face space to
compensate. Compared to baseline algorithms, extensive experiments demonstrate
that the proposed method achieves a higher performance both quantitatively and
qualitatively. Codes are at https://github.com/kgu3/FLNet_AAAI2020.
