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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Recent advances in differentiable rendering have sparked an interest inlearning generative models of textured 3D meshes from image collections. Thesemodels natively disentangle pose and appearance, enable downstream applicationsin computer graphics, and improve the ability of generative models tounderstand the concept of image formation. Although there has been prior workon learning such models from collections of 2D images, these approaches requirea delicate pose estimation step that exploits annotated keypoints, therebyrestricting their applicability to a few specific datasets. In this work, wepropose a GAN framework for generating textured triangle meshes without relyingon such annotations. We show that the performance of our approach is on parwith prior work that relies on ground-truth keypoints, and more importantly, wedemonstrate the generality of our method by setting new baselines on a largerset of categories from ImageNet - for which keypoints are not available -without any class-specific hyperparameter tuning. We release our code athttps://github.com/dariopavllo/textured-3d-gan

Learning Generative Models of Textured 3D Meshes from Real-World Images