Facial Prior Based First Order Motion Model for Micro-expression
  Generation
Yi Zhang Youjun Zhao Yuhang Wen Zixuan Tang Xinhua Xu Mengyuan Liu
Spotting facial micro-expression from videos finds various potential
applications in fields including clinical diagnosis and interrogation,
meanwhile this task is still difficult due to the limited scale of training
data. To solve this problem, this paper tries to formulate a new task called
micro-expression generation and then presents a strong baseline which combines
the first order motion model with facial prior knowledge. Given a target face,
we intend to drive the face to generate micro-expression videos according to
the motion patterns of source videos. Specifically, our new model involves
three modules. First, we extract facial prior features from a region focusing
module. Second, we estimate facial motion using key points and local affine
transformations with a motion prediction module. Third, expression generation
module is used to drive the target face to generate videos. We train our model
on public CASME II, SAMM and SMIC datasets and then use the model to generate
new micro-expression videos for evaluation. Our model achieves the first place
in the Facial Micro-Expression Challenge 2021 (MEGC2021), where our superior
performance is verified by three experts with Facial Action Coding System
certification. Source code is provided in
https://github.com/Necolizer/Facial-Prior-Based-FOMM.