Deep neural networks (DNNs) have shown remarkable performance improvements on
vision-related tasks such as object detection or image segmentation. Despite their success …

In the rapidly advancing field of multi-modal machine learning (MMML), the convergence of
multiple data modalities has the potential to reshape various applications. This paper …

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 …

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 …

Abstract Neural Radiance Fields (NeRF) have emerged as a powerful representation for the
task of novel view synthesis due to their simplicity and state-of-the-art performance. Though …

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 …

Neural implicit 3D representations have emerged as a powerful paradigm for reconstructing
surfaces from multi-view images and synthesizing novel views. Unfortunately, existing …

We propose pixelNeRF, a learning framework that predicts a continuous neural scene
representation conditioned on one or few input images. The existing approach for …

Neural rendering techniques combining machine learning with geometric reasoning have
arisen as one of the most promising approaches for synthesizing novel views of a scene …