We introduce k-planes, a white-box model for radiance fields in arbitrary dimensions. Our
model uses d-choose-2 planes to represent a d-dimensional scene, providing a seamless …

Modeling and re-rendering dynamic 3D scenes is a challenging task in 3D vision. Prior
approaches build on NeRF and rely on implicit representations. This is slow since it requires …

Implicit neural representation has paved the way for new approaches to dynamic scene
reconstruction. Nonetheless cutting-edge dynamic neural rendering methods rely heavily on …

Text-guided diffusion models have revolutionized image and video generation and have
also been successfully used for optimization-based 3D object synthesis. Here we instead …

Abstract 3D reconstruction of deformable (or non‐rigid) scenes from a set of monocular 2D
image observations is a long‐standing and actively researched area of computer vision and …

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 …

We address the problem of synthesizing novel views from a monocular video depicting a
complex dynamic scene. State-of-the-art methods based on temporally varying Neural …

Recent methods for neural surface representation and rendering, for example NeuS, have
demonstrated the remarkably high-quality reconstruction of static scenes. However, the …

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 …

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 …