Recent advances in machine learning have led to increased interest in solving visual
computing problems using methods that employ coordinate‐based neural networks. These …

Implicit neural fields, typically encoded by a multilayer perceptron (MLP) that maps from
coordinates (eg, xyz) to signals (eg, signed distances), have shown remarkable promise as …

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 …

Humans are able to accurately reason in 3D by gathering multi-view observations of the
surrounding world. Inspired by this insight, we introduce a new large-scale benchmark for …

We propose a pipeline to generate Neural Radiance Fields (NeRF) of an object or a scene
of a specific class, conditioned on a single input image. This is a challenging task, as …

We propose CLIP-Actor, a text-driven motion recommendation and neural mesh stylization
system for human mesh animation. CLIP-Actor animates a 3D human mesh to conform to a …

Our environment is filled with rich and dynamic acoustic information. When we walk into a
cathedral, the reverberations as much as appearance inform us of the sanctuary's wide open …

Abstract Implicit Neural Spatial Representation (INSR) has emerged as an effective
representation of spatially-dependent vector fields. This work explores solving time …

The long runtime of high-fidelity partial differential equation (PDE) solvers makes them
unsuitable for time-critical applications. We propose to accelerate PDE solvers using …

Implicit Neural Representations (INRs) have emerged in the last few years as a powerful tool
to encode continuously a variety of different signals like images, videos, audio and 3D …