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

We present Face Swapping GAN (FSGAN) for face swapping and reenactment.Unlike previous work, we offer a subject agnostic swapping scheme that can beapplied to pairs of faces without requiring training on those faces. We derivea novel iterative deep learning--based approach for face reenactment whichadjusts significant pose and expression variations that can be applied to asingle image or a video sequence. For video sequences, we introduce acontinuous interpolation of the face views based on reenactment, DelaunayTriangulation, and barycentric coordinates. Occluded face regions are handledby a face completion network. Finally, we use a face blending network forseamless blending of the two faces while preserving the target skin color andlighting conditions. This network uses a novel Poisson blending loss combiningPoisson optimization with a perceptual loss. We compare our approach toexisting state-of-the-art systems and show our results to be both qualitativelyand quantitatively superior. This work describes extensions of the FSGANmethod, proposed in an earlier conference version of our work, as well asadditional experiments and results.

FSGANv2: Improved Subject Agnostic Face Swapping and Reenactment