Existing 3D-aware facial generation methods face a dilemma in quality versus editability:
they either generate editable results in low resolution, or high-quality ones with no editing …

Abstract 3D-controllable portrait synthesis has significantly advanced, thanks to
breakthroughs in generative adversarial networks (GANs). However, it is still challenging to …

We present a single-image 3D face synthesis technique that can handle challenging facial
expressions while recovering fine geometric details. Our technique employs expression …

Although 2D generative models have made great progress in face image generation and
animation, they often suffer from undesirable artifacts such as 3D inconsistency when …

Emerging Metaverse applications demand accessible, accurate, and easy-to-use tools for
3D digital human creations in order to depict different cultures and societies as if in the …

Abstract 3D-aware generative adversarial networks (GANs) synthesize high-fidelity and
multi-view-consistent facial images using only collections of single-view 2D imagery …

Generating realistic 3D faces is of high importance for computer graphics and computer
vision applications. Generally, research on 3D face generation revolves around linear …

Previous portrait image generation methods roughly fall into two categories: 2D GANs and
3D-aware GANs. 2D GANs can generate high fidelity portraits but with low view consistency …

The manipulation of latent space has recently become an interesting topic in the field of
generative models. Recent research shows that latent directions can be used to manipulate …

We propose a framework, called LiftedGAN, that disentangles and lifts a pre-trained
StyleGAN2 for 3D-aware face generation. Our model is" 3D-aware" in the sense that it is …