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.
Micro-expression generation aims at transfering the expression from the driving videos to the source images, which can be viewed as a motion transfer task. Recently, several works have been proposed to tackle this problem and achieve great performance. However, due to the intrinsic complexity of the face motion and different attributes of face regions, the task still remains challenging. In this paper, we propose an end-to-end unsupervised motion transfer network to tackle this challenge. As the motion of the face is non-rigid, we adopt an effective and flexible thin-plate spline motion estimation method to estimate the optical flow of the face motion. What's more, we find that several faces with eyeglasses show weird deformation in motion transfering. Thus, we introduce face parsing method to pay specific attention to the eyeglasses regions to ensure the reasonability of the deformation. We conduct several experiments on the provided datasets of the ACM MM 2022 micro-expression grand challenge (MEGC2022) and compare our method with several other typical methods. In comparison, our method shows the best performance. We (Team: USTC-IAT-United) also compare our method with other competitors' in MEGC2022, and the expert evaluation results show that our method performs best, which verifies the effectiveness of our method. Our code is available at https://github.com/HowToNameMe/micro-expression