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

Reconstruction of the shape and motion of humans from RGB-D is a challengingproblem, receiving much attention in recent years. Recent approaches forfull-body reconstruction use a statistic shape model, which is built uponaccurate full-body scans of people in skin-tight clothes, to complete invisibleparts due to occlusion. Such a statistic model may still be fit to an RGB-Dmeasurement with loose clothes but cannot describe its deformations, such asclothing wrinkles. Observed surfaces may be reconstructed precisely from actualmeasurements, while we have no cues for unobserved surfaces. For full-bodyreconstruction with loose clothes, we propose to use lower dimensionalembeddings of texture and deformation referred to as eigen-texturing andeigen-deformation, to reproduce views of even unobserved surfaces. Provided afull-body reconstruction from a sequence of partial measurements as 3D meshes,the texture and deformation of each triangle are then embedded usingeigen-decomposition. Combined with neural-network-based coefficient regression,our method synthesizes the texture and deformation from arbitrary viewpoints.We evaluate our method using simulated data and visually demonstrate how ourmethod works on real data.

Representing a Partially Observed Non-Rigid 3D Human Using Eigen-Texture and Eigen-Deformation