The standard approach to tackling computer vision problems is to train deep convolutional
neural network (CNN) models using large-scale image datasets that are representative of …

A diffusion model learns to predict a vector field of gradients. We propose to apply chain rule
on the learned gradients, and back-propagate the score of a diffusion model through the …

In this work, we present a novel framework built to simplify 3D asset generation for amateur
users. To enable interactive generation, our method supports a variety of input modalities …

Recent years have seen remarkable progress in deep learning powered visual content
creation. This includes deep generative 3D-aware image synthesis, which produces high …

Implicit neural fields, typically encoded by a multilayer perceptron (MLP) that maps from
coordinates (eg, xyz) to signals (eg, signed distances), have shown remarkable promise as …

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

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 …

We address the problem of learning person-specific facial priors from a small number (eg,
20) of portrait photos of the same person. This enables us to edit this specific person's facial …

Inverse graphics aims to recover 3D models from 2D observations. Utilizing differentiable
rendering, recent 3D-aware generative models have shown impressive results of rigid object …

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 …