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

Editing of portrait images is a very popular and important research topicwith a large variety of applications. For ease of use, control should beprovided via a semantically meaningful parameterization that is akin tocomputer animation controls. The vast majority of existing techniques do notprovide such intuitive and fine-grained control, or only enable coarse editingof a single isolated control parameter. Very recently, high-qualitysemantically controlled editing has been demonstrated, however only onsynthetically created StyleGAN images. We present the first approach forembedding real portrait images in the latent space of StyleGAN, which allowsfor intuitive editing of the head pose, facial expression, and sceneillumination in the image. Semantic editing in parameter space is achievedbased on StyleRig, a pretrained neural network that maps the control space of a3D morphable face model to the latent space of the GAN. We design a novelhierarchical non-linear optimization problem to obtain the embedding. Anidentity preservation energy term allows spatially coherent edits whilemaintaining facial integrity. Our approach runs at interactive frame rates andthus allows the user to explore the space of possible edits. We evaluate ourapproach on a wide set of portrait photos, compare it to the current state ofthe art, and validate the effectiveness of its components in an ablation study.

PIE: Portrait Image Embedding for Semantic Control