Light field imaging has emerged as a technology allowing to capture richer visual
information from our world. As opposed to traditional photography, which captures a 2D …

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 super-fast convergence approach to reconstructing the per-scene radiance
field from a set of images that capture the scene with known poses. This task, which is often …

This paper presents a novel grid-based NeRF called F^ 2-NeRF (Fast-Free-NeRF) for novel
view synthesis, which enables arbitrary input camera trajectories and only costs a few …

The rendering procedure used by neural radiance fields (NeRF) samples a scene with a
single ray per pixel and may therefore produce renderings that are excessively blurred or …

Neural volumetric representations such as Neural Radiance Fields (NeRF) have emerged
as a compelling technique for learning to represent 3D scenes from images with the goal of …

This paper addresses the challenge of novel view synthesis for a human performer from a
very sparse set of camera views. Some recent works have shown that learning implicit …

We present a method to perform novel view and time synthesis of dynamic scenes, requiring
only a monocular video with known camera poses as input. To do this, we introduce Neural …

Abstract 3D scene stylization aims at generating stylized images of the scene from arbitrary
novel views following a given set of style examples, while ensuring consistency when …

We propose a novel approach for 3D video synthesis that is able to represent multi-view
video recordings of a dynamic real-world scene in a compact, yet expressive representation …