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

Currently it requires an artist to create 3D human avatars with realisticclothing that can move naturally. Despite progress on 3D scanning and modelingof human bodies, there is still no technology that can easily turn a staticscan into an animatable avatar. Automating the creation of such avatars wouldenable many applications in games, social networking, animation, and AR/VR toname a few. The key problem is one of representation. Standard 3D meshes arewidely used in modeling the minimally-clothed body but do not readily capturethe complex topology of clothing. Recent interest has shifted to implicitsurface models for this task but they are computationally heavy and lackcompatibility with existing 3D tools. What is needed is a 3D representationthat can capture varied topology at high resolution and that can be learnedfrom data. We argue that this representation has been with us all along -- thepoint cloud. Point clouds have properties of both implicit and explicitrepresentations that we exploit to model 3D garment geometry on a human body.We train a neural network with a novel local clothing geometric feature torepresent the shape of different outfits. The network is trained from 3D pointclouds of many types of clothing, on many bodies, in many poses, and learns tomodel pose-dependent clothing deformations. The geometry feature can beoptimized to fit a previously unseen scan of a person in clothing, enabling thescan to be reposed realistically. Our model demonstrates superior quantitativeand qualitative results in both multi-outfit modeling and unseen outfitanimation. The code is available for research purposes.

The Power of Points for Modeling Humans in Clothing