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 recent years, there has been significant progress in 2D generative facemodels fueled by applications such as animation, synthetic data generation, anddigital avatars. However, due to the absence of 3D information, these 2D modelsoften struggle to accurately disentangle facial attributes like pose,expression, and illumination, limiting their editing capabilities. To addressthis limitation, this paper proposes a 3D controllable generative face model toproduce high-quality albedo and precise 3D shape leveraging existing 2Dgenerative models. By combining 2D face generative models with semantic facemanipulation, this method enables editing of detailed 3D rendered faces. Theproposed framework utilizes an alternating descent optimization approach overshape and albedo. Differentiable rendering is used to train high-quality shapesand albedo without 3D supervision. Moreover, this approach outperforms thestate-of-the-art (SOTA) methods in the well-known NoW benchmark for shapereconstruction. It also outperforms the SOTA reconstruction models inrecovering rendered faces' identities across novel poses by an average of 10%.Additionally, the paper demonstrates direct control of expressions in 3D facesby exploiting latent space leading to text-based editing of 3D faces.

Towards Realistic Generative 3D Face Models