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

Creating plausible virtual actors from images of real actors remains one ofthe key challenges in computer vision and computer graphics. Marker-less humanmotion estimation and shape modeling from images in the wild bring thischallenge to the fore. Although the recent advances on view synthesis andimage-to-image translation, currently available formulations are limited totransfer solely style and do not take into account the character's motion andshape, which are by nature intermingled to produce plausible human forms. Inthis paper, we propose a unifying formulation for transferring appearance andretargeting human motion from monocular videos that regards all these aspects.Our method synthesizes new videos of people in a different context where theywere initially recorded. Differently from recent appearance transferringmethods, our approach takes into account body shape, appearance, and motionconstraints. The evaluation is performed with several experiments usingpublicly available real videos containing hard conditions. Our method is ableto transfer both human motion and appearance outperforming state-of-the-artmethods, while preserving specific features of the motion that must bemaintained (e.g., feet touching the floor, hands touching a particular object)and holding the best visual quality and appearance metrics such as StructuralSimilarity (SSIM) and Learned Perceptual Image Patch Similarity (LPIPS).

Do As I Do: Transferring Human Motion and Appearance between Monocular Videos with Spatial and Temporal Constraints