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 …

Humans describe the physical world using natural language to refer to specific 3D locations
based on a vast range of properties: visual appearance, semantics, abstract associations, or …

We present ScanNet++, a large-scale dataset that couples together capture of high-quality
and commodity-level geometry and color of indoor scenes. Each scene is captured with a …

We extend neural radiance fields (NeRFs) to dynamic large-scale urban scenes. Prior work
tends to reconstruct single video clips of short durations (up to 10 seconds). Two reasons …

Abstract Optimizing and rendering Neural Radiance Fields is computationally expensive
due to the vast number of samples required by volume rendering. Recent works have …

We introduce PhysGaussian a new method that seamlessly integrates physically grounded
Newtonian dynamics within 3D Gaussians to achieve high-quality novel motion synthesis …

Neural radiance fields (NeRF) and its subsequent variants have led to remarkable progress
in neural rendering. While most of recent neural rendering works focus on objects and small …

Abstract Neural Radiance Fields (NeRFs) have shown promise in applications like view
synthesis and depth estimation but learning from multiview images faces inherent …

Abstract Casually captured Neural Radiance Fields (NeRFs) suffer from artifacts such as
floaters or flawed geometry when rendered outside the input camera trajectory. Existing …

We focus on reconstructing high-fidelity radiance fields of human heads, capturing their
animations over time, and synthesizing re-renderings from novel viewpoints at arbitrary time …