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 …

The field of visual computing is rapidly advancing due to the emergence of generative
artificial intelligence (AI), which unlocks unprecedented capabilities for the generation …

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 …

Abstract Neural Radiance Field training can be accelerated through the use of grid-based
representations in NeRF's learned mapping from spatial coordinates to colors and …

Representing and rendering dynamic scenes has been an important but challenging task.
Especially to accurately model complex motions high efficiency is usually hard to guarantee …

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 …

A diffusion model learns to predict a vector field of gradients. We propose to apply chain rule
on the learned gradients, and back-propagate the score of a diffusion model through the …

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 …

Neural surface reconstruction has been shown to be powerful for recovering dense 3D
surfaces via image-based neural rendering. However, current methods struggle to recover …