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

In this paper, we present a simple yet effective method to automaticallytransfer textures of clothing images (front and back) to 3D garments worn ontop SMPL, in real time. We first automatically compute training pairs of imageswith aligned 3D garments using a custom non-rigid 3D to 2D registration method,which is accurate but slow. Using these pairs, we learn a mapping from pixelsto the 3D garment surface. Our idea is to learn dense correspondences fromgarment image silhouettes to a 2D-UV map of a 3D garment surface using shapeinformation alone, completely ignoring texture, which allows us to generalizeto the wide range of web images. Several experiments demonstrate that our modelis more accurate than widely used baselines such as thin-plate-spline warpingand image-to-image translation networks while being orders of magnitude faster.Our model opens the door for applications such as virtual try-on, and allowsfor generation of 3D humans with varied textures which is necessary forlearning.

Learning to Transfer Texture from Clothing Images to 3D Humans