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

Unsupervised learning of 3D-aware generative adversarial networks has latelymade much progress. Some recent work demonstrates promising results of learninghuman generative models using neural articulated radiance fields, yet theirgeneralization ability and controllability lag behind parametric human models,i.e., they do not perform well when generalizing to novel pose/shape and arenot part controllable. To solve these problems, we propose VeRi3D, a generativehuman vertex-based radiance field parameterized by vertices of the parametrichuman template, SMPL. We map each 3D point to the local coordinate systemdefined on its neighboring vertices, and use the corresponding vertex featureand local coordinates for mapping it to color and density values. Wedemonstrate that our simple approach allows for generating photorealistic humanimages with free control over camera pose, human pose, shape, as well asenabling part-level editing.

VeRi3D: Generative Vertex-based Radiance Fields for 3D Controllable Human Image Synthesis