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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In this paper, we present an integrated system for automatically generatingand editing face images through face swapping, attribute-based editing, andrandom face parts synthesis. The proposed system is based on a deep neuralnetwork that variationally learns the face and hair regions with large-scaleface image datasets. Different from conventional variational methods, theproposed network represents the latent spaces individually for faces and hairs.We refer to the proposed network as region-separative generative adversarialnetwork (RSGAN). The proposed network independently handles face and hairappearances in the latent spaces, and then, face swapping is achieved byreplacing the latent-space representations of the faces, and reconstruct theentire face image with them. This approach in the latent space robustlyperforms face swapping even for images which the previous methods result infailure due to inappropriate fitting or the 3D morphable models. In addition,the proposed system can further edit face-swapped images with the same networkby manipulating visual attributes or by composing them with randomly generatedface or hair parts.

RSGAN: Face Swapping and Editing using Face and Hair Representation in Latent Spaces