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 …

Abstract We introduce Zero-1-to-3, a framework for changing the camera viewpoint of an
object given just a single RGB image. To perform novel view synthesis in this …

Single image 3D reconstruction is an important but challenging task that requires extensive
knowledge of our natural world. Many existing methods solve this problem by optimizing a …

Natural language processing and 2D vision models have attained remarkable proficiency on
many tasks primarily by escalating the scale of training data. However, 3D vision tasks have …

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 …

Neural Radiance Fields (NeRF) are a rapidly growing area of research with wide-ranging
applications in computer vision, graphics, robotics, and more. In order to streamline the …

We consider the problem of reconstructing a full 360deg photographic model of an object
from a single image of it. We do so by fitting a neural radiance field to the image, but find this …

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 …

Recent advancements in open-world 3D object generation have been remarkable with
image-to-3D methods offering superior fine-grained control over their text-to-3D …

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 …