Videos show continuous events, yet most--if not all--video synthesis frameworks treat them
discretely in time. In this work, we think of videos of what they should be--time-continuous …

A recent trend in generative modeling is building 3D-aware generators from 2D image
collections. To induce the 3D bias, such models typically rely on volumetric rendering, which …

Abstract Recently, 3D Gaussian splatting (3D-GS) has gained popularity in novel-view
scene synthesis. It addresses the challenges of lengthy training times and slow rendering …

We present a novel type of neural fields that uses general radial bases for signal
representation. State-of-the-art neural fields typically rely on grid-based representations for …

The following three factors restrict the application of existing low-light image enhancement
methods: unpredictable brightness degradation and noise, inherent gap between metric …

Implicit neural representations (INRs) have emerged as a powerful paradigm in scene
reconstruction and computer graphics, showcasing remarkable results. By utilizing neural …

It is common practice in deep learning to represent a measurement of the world on a
discrete grid, eg a 2D grid of pixels. However, the underlying signal represented by these …

Abstract Implicit Neural Representations (INRs) gained attention as a novel and effective
representation for various data types. Recently, prior work applied INRs to image …

We present Generalizable NeRF Transformer (GNT), a transformer-based architecture that
reconstructs Neural Radiance Fields (NeRFs) and learns to renders novel views on the fly …

Representing visual signals by implicit neural representation (INR) has prevailed among
many vision tasks. Its potential for editing/processing given signals remains less explored …