  Face animation has received a lot of attention from researchers in recent
years due to its wide range of promising applications. Many face animation
models based on optical flow or deep neural networks have achieved great
success. However, these models are likely to fail in animated scenarios with
significant view changes, resulting in unrealistic or distorted faces. One of
the possible reasons is that such models lack prior knowledge of human faces
and are not proficient to imagine facial regions they have never seen before.
In this paper, we propose a flexible and generic approach to improve the
performance of face animation without additional training. We use multiple
source images as input as compensation for the lack of prior knowledge of
faces. The effectiveness of our method is experimentally demonstrated, where
the proposed method successfully supplements the baseline method.
