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 …

We propose a method for editing NeRF scenes with text-instructions. Given a NeRF of a
scene and the collection of images used to reconstruct it, our method uses an image …

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 …

Emerging neural radiance fields (NeRF) are a promising scene representation for computer
graphics, enabling high-quality 3D reconstruction and novel view synthesis from image …

Implicit neural rendering, especially Neural Radiance Field (NeRF), has shown great
potential in novel view synthesis of a scene. However, current NeRF-based methods cannot …

We present CLIP-NeRF, a multi-modal 3D object manipulation method for neural radiance
fields (NeRF). By leveraging the joint language-image embedding space of the recent …

Abstract 3D editing plays a crucial role in many areas such as gaming and virtual reality.
Traditional 3D editing methods which rely on representations like meshes and point clouds …

A commonly observed failure mode of Neural Radiance Field (NeRF) is fitting incorrect
geometries when given an insufficient number of input views. One potential reason is that …

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 …