English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Spotting facial micro-expression from videos finds various potentialapplications in fields including clinical diagnosis and interrogation,meanwhile this task is still difficult due to the limited scale of trainingdata. To solve this problem, this paper tries to formulate a new task calledmicro-expression generation and then presents a strong baseline which combinesthe first order motion model with facial prior knowledge. Given a target face,we intend to drive the face to generate micro-expression videos according tothe motion patterns of source videos. Specifically, our new model involvesthree modules. First, we extract facial prior features from a region focusingmodule. Second, we estimate facial motion using key points and local affinetransformations with a motion prediction module. Third, expression generationmodule is used to drive the target face to generate videos. We train our modelon public CASME II, SAMM and SMIC datasets and then use the model to generatenew micro-expression videos for evaluation. Our model achieves the first placein the Facial Micro-Expression Challenge 2021 (MEGC2021), where our superiorperformance is verified by three experts with Facial Action Coding Systemcertification. Source code is provided inhttps://github.com/Necolizer/Facial-Prior-Based-FOMM.

Facial Prior Based First Order Motion Model for Micro-expression Generation