Neural Radiance Fields (NeRF) is a popular view synthesis technique that represents a
scene as a continuous volumetric function, parameterized by multilayer perceptrons that …

We present an algorithm for generating novel views at arbitrary viewpoints and any input
time step given a monocular video of a dynamic scene. Our work builds upon recent …

We present a practical and robust deep learning solution for capturing and rendering novel
views of complex real world scenes for virtual exploration. Previous approaches either …

View synthesis allows for the generation of new views of a scene given one or more images.
This is challenging; it requires comprehensively understanding the 3D scene from images …

We propose a method for converting a single RGB-D input image into a 3D photo, ie, a multi-
layer representation for novel view synthesis that contains hallucinated color and depth …

We present a new flow-based video completion algorithm. Previous flow completion
methods are often unable to retain the sharpness of motion boundaries. Our method first …

We present a novel algorithm for view synthesis that utilizes a soft 3D reconstruction to
improve quality, continuity and robustness. Our main contribution is the formulation of a soft …

View-dependent effects such as reflections pose a substantial challenge for image-based
and neural rendering algorithms. Above all, curved reflectors are particularly hard, as they …

The introduction of neural radiance fields has greatly improved the effectiveness of view
synthesis for monocular videos. However, existing algorithms face difficulties when dealing …

We suggest to represent an X-Field---a set of 2D images taken across different view, time or
illumination conditions, ie, video, lightfield, reflectance fields or combinations thereof---by …