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 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 …

Dynamic radiance field reconstruction methods aim to model the time-varying structure and
appearance of a dynamic scene. Existing methods, however, assume that accurate camera …

Neural Radiance Fields (NeRF) are able to reconstruct scenes with unprecedented fidelity,
and various recent works have extended NeRF to handle dynamic scenes. A common …

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 rendering techniques combining machine learning with geometric reasoning have
arisen as one of the most promising approaches for synthesizing novel views of a scene …

We present the first method capable of photorealistically reconstructing deformable scenes
using photos/videos captured casually from mobile phones. Our approach augments neural …

We present a method to perform novel view and time synthesis of dynamic scenes, requiring
only a monocular video with known camera poses as input. To do this, we introduce Neural …

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 …

Abstract We present Non-Rigid Neural Radiance Fields (NR-NeRF), a reconstruction and
novel view synthesis approach for general non-rigid dynamic scenes. Our approach takes …