Recently, generative adversarial networks (GANs) have progressed enormously, which
makes them able to learn complex data distributions in particular faces. More and more …

Abstract Generative Adversarial Networks (GAN) have led to the generation of very realistic
face images, which have been used in fake social media accounts and other disinformation …

Synthesizing visual content that meets users' needs often requires flexible and precise
controllability of the pose, shape, expression, and layout of the generated objects. Existing …

Recently, numerous facial editing techniques have been proposed that leverage the
generative power of a pretrained StyleGAN. To successfully edit an image this way, one …

Synthesizing photo‐realistic images and videos is at the heart of computer graphics and has
been the focus of decades of research. Traditionally, synthetic images of a scene are …

We present MVSNeRF, a novel neural rendering approach that can efficiently reconstruct
neural radiance fields for view synthesis. Unlike prior works on neural radiance fields that …

GAN inversion aims to invert a given image back into the latent space of a pretrained GAN
model so that the image can be faithfully reconstructed from the inverted code by the …

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 …

One-shot talking face generation aims at synthesizing a high-quality talking face video from
an arbitrary portrait image, driven by a video or an audio segment. In this work, we provide a …

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 …