Neural radiance fields enable state-of-the-art photorealistic view synthesis. However,
existing radiance field representations are either too compute-intensive for real-time …

Abstract Neural Radiance Fields (NeRFs) have demonstrated amazing ability to synthesize
images of 3D scenes from novel views. However, they rely upon specialized volumetric …

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

Synthesizing photo‐realistic images and videos is at the heart of computer graphics and has
been the focus of decades of research. Traditionally, synthetic images of a scene are …

Neural Radiance Fields (NeRF) achieve impressive view synthesis results for a variety of
capture settings, including 360 capture of bounded scenes and forward-facing capture of …

We present an algorithm for generating novel views at arbitrary viewpoints and any input
time step given a monocular video of a dynamic scene. Our work builds upon recent …

Novel view synthesis of dynamic scenes has been an intriguing yet challenging problem.
Despite recent advancements simultaneously achieving high-resolution photorealistic …

Volumetric scene representations enable photorealistic view synthesis for static scenes and
form the basis of several existing 6-DoF video techniques. However, the volume rendering …

We present a method that learns a spatiotemporal neural irradiance field for dynamic scenes
from a single video. Our learned representation enables free-viewpoint rendering of the …

Coordinate-based networks have emerged as a powerful tool for 3D representation and
scene reconstruction. These networks are trained to map continuous input coordinates to the …