Text-to-image personalization aims to teach a pre-trained diffusion model to reason about
novel, user provided concepts, embedding them into new scenes guided by natural …

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 …

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 …

We present a high-fidelity 3D generative adversarial network (GAN) inversion framework
that can synthesize photo-realistic novel views while preserving specific details of the input …

Text-to-image (T2I) personalization allows users to guide the creative image generation
process by combining their own visual concepts in natural language prompts. Recently …

In this paper, we present our method for neural face reenactment, called HyperReenact, that
aims to generate realistic talking head images of a source identity, driven by a target facial …

Recently, a surge of high-quality 3D-aware GANs have been proposed, which leverage the
generative power of neural rendering. It is natural to associate 3D GANs with GAN inversion …

Deep generative models, which target reproducing the given data distribution to produce
novel samples, have made unprecedented advancements in recent years. Their technical …

In contrast to fully connected networks, Convolutional Neural Networks (CNNs) achieve
efficiency by learning weights associated with local filters with a finite spatial extent …

Existing methods for 3D-aware image synthesis largely depend on the 3D pose distribution
pre-estimated on the training set. An inaccurate estimation may mislead the model into …