Neural radiance fields achieve unprecedented quality for novel view synthesis, but their
volumetric formulation remains expensive, requiring a huge number of samples to render …

We present an end-to-end system for the high-fidelity capture, model reconstruction, and
real-time rendering of walkable spaces in virtual reality using neural radiance fields. To this …

In recent years, Neural Radiance Fields (NeRF) has made remarkable progress in the field
of computer vision and graphics, providing strong technical support for solving key tasks …

Recent techniques for real-time view synthesis have rapidly advanced in fidelity and speed,
and modern methods are capable of rendering near-photorealistic scenes at interactive …

This paper aims to tackle the challenge of dynamic view synthesis from multi-view videos.
The key observation is that while previous grid-based methods offer consistent rendering …

The increased demand for 3D data in AR/VR robotics and gaming applications gave rise to
powerful generative pipelines capable of synthesizing high-quality 3D objects. Most of these …

Abstract Neural Radiance Fields (NeRFs) excel in photorealistically rendering static scenes.
However rendering dynamic long-duration radiance fields on ubiquitous devices remains …

Real-time novel-view image synthesis on mobile devices is prohibitive due to the limited
computational power and storage. Using volumetric rendering methods, such as NeRF and …

We present a method to build animatable dog avatars from monocular videos. This is
challenging as animals display a range of (unpredictable) non-rigid movements and have a …

Existing neural radiance field-based methods can achieve real-time rendering of small
scenes on the web platform. However, extending these methods to large-scale scenes still …