Scientific discovery and engineering design are currently limited by the time and cost of
physical experiments. Numerical simulations are an alternative approach but are usually …

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 …

Diffusion models have emerged as the state-of-the-art for image generation, among other
tasks. Here, we present an efficient diffusion-based model for 3D-aware generation of neural …

Rigorously testing autonomy systems is essential for making safe self-driving vehicles (SDV)
a reality. It requires one to generate safety critical scenarios beyond what can be collected …

We propose a method to allow precise and extremely fast mesh extraction from 3D Gaussian
Splatting. Gaussian Splatting has recently become very popular as it yields realistic …

Abstract We present Block-NeRF, a variant of Neural Radiance Fields that can represent
large-scale environments. Specifically, we demonstrate that when scaling NeRF to render …

Unsupervised generation of high-quality multi-view-consistent images and 3D shapes using
only collections of single-view 2D photographs has been a long-standing challenge …

We introduce a free-viewpoint rendering method--HumanNeRF--that works on a given
monocular video of a human performing complex body motions, eg a video from YouTube …

Though neural radiance fields (" NeRF") have demonstrated impressive view synthesis
results on objects and small bounded regions of space, they struggle on" unbounded" …

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 …