Recent years have witnessed the tremendous progress of 3D GANs for generating view-
consistent radiance fields with photo-realism. Yet, high-quality generation of human …

Abstract We propose LIRF (Local Implicit Ray Function), a generalizable neural rendering
approach for novel view rendering. Current generalizable neural radiance fields (NeRF) …

Existing 3D-from-2D generators are typically designed for well-curated single-category
datasets, where all the objects have (approximately) the same scale, 3D location, and …

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 …

We propose a novel approach for unsupervised 3D animation of non-rigid deformable
objects. Our method learns the 3D structure and dynamics of objects solely from single-view …

We present a new method for generating realistic and view-consistent images with fine
geometry from 2D image collections. Our method proposes a hybrid explicit-implicit …

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 …

Abstract 3D-aware Generative Adversarial Networks (GANs) have shown remarkable
progress in learning to generate multi-view-consistent images and 3D geometries of scenes …

We introduce VIVE3D, a novel approach that extends the capabilities of image-based 3D
GANs to video editing and is able to represent the input video in an identity-preserving and …

Previous animatable 3D-aware GANs for human generation have primarily focused on
either the human head or full body. However, head-only videos are relatively uncommon in …