Neural Radiance Field (NeRF), a new novel view synthesis with implicit scene
representation has taken the field of Computer Vision by storm. As a novel view synthesis …

Efficient rendering of photo‐realistic virtual worlds is a long standing effort of computer
graphics. Modern graphics techniques have succeeded in synthesizing photo‐realistic …

We introduce k-planes, a white-box model for radiance fields in arbitrary dimensions. Our
model uses d-choose-2 planes to represent a d-dimensional scene, providing a seamless …

Modeling and re-rendering dynamic 3D scenes is a challenging task in 3D vision. Prior
approaches build on NeRF and rely on implicit representations. This is slow since it requires …

In this work, we investigate the problem of creating high-fidelity 3D content from only a single
image. This is inherently challenging: it essentially involves estimating the underlying 3D …

Abstract Neural Radiance Fields (NeRFs) have demonstrated amazing ability to synthesize
images of 3D scenes from novel views. However, they rely upon specialized volumetric …

We present a diffusion-based model for 3D-aware generative novel view synthesis from as
few as a single input image. Our model samples from the distribution of possible renderings …

We present TensoRF, a novel approach to model and reconstruct radiance fields. Unlike
NeRF that purely uses MLPs, we model the radiance field of a scene as a 4D tensor, which …

Novel view synthesis with sparse inputs is a challenging problem for neural radiance fields
(NeRF). Recent efforts alleviate this challenge by introducing external supervision, such as …

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 …