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

Recent studies on facial expression editing have obtained very promisingprogress. On the other hand, existing methods face the constraint of requiringa large amount of expression labels which are often expensive andtime-consuming to collect. This paper presents an innovative label-freeexpression editing via disentanglement (LEED) framework that is capable ofediting the expression of both frontal and profile facial images withoutrequiring any expression label. The idea is to disentangle the identity andexpression of a facial image in the expression manifold, where the neutral facecaptures the identity attribute and the displacement between the neutral imageand the expressive image captures the expression attribute. Two novel lossesare designed for optimal expression disentanglement and consistent synthesis,including a mutual expression information loss that aims to extract pureexpression-related features and a siamese loss that aims to enhance theexpression similarity between the synthesized image and the reference image.Extensive experiments over two public facial expression datasets show that LEEDachieves superior facial expression editing qualitatively and quantitatively.

LEED: Label-Free Expression Editing via Disentanglement