Reconstruction of a continuous surface of two-dimensional manifold from its raw, discrete
point cloud observation is a long-standing problem in computer vision and graphics …

Diffusion models have emerged as the state-of-the-art for image generation, among other
tasks. Here, we present an efficient diffusion-based model for 3D-aware generation of neural …

We present PanopticNeRF, an object-aware neural scene representation that decomposes
a scene into a set of objects (things) and background (stuff). Each object is represented by a …

Unsupervised generation of high-quality multi-view-consistent images and 3D shapes using
only collections of single-view 2D photographs has been a long-standing challenge …

We introduce a high resolution, 3D-consistent image and shape generation technique which
we call StyleSDF. Our method is trained on single view RGB data only, and stands on the …

We introduce DMTet, a deep 3D conditional generative model that can synthesize high-
resolution 3D shapes using simple user guides such as coarse voxels. It marries the merits …

Recent advances in machine learning have led to increased interest in solving visual
computing problems using methods that employ coordinate‐based neural networks. These …

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 …

Unsupervised learning of 3D-aware generative adversarial networks (GANs) using only
collections of single-view 2D photographs has very recently made much progress. These 3D …

We have witnessed rapid progress on 3D-aware image synthesis, leveraging recent
advances in generative visual models and neural rendering. Existing approaches however …