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 MVSNeRF, a novel neural rendering approach that can efficiently reconstruct
neural radiance fields for view synthesis. Unlike prior works on neural radiance fields that …

This paper addresses the challenge of novel view synthesis for a human performer from a
very sparse set of camera views. Some recent works have shown that learning implicit …

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 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 …

Considering the problem of novel view synthesis (NVS) from only a set of 2D images, we
simplify the training process of Neural Radiance Field (NeRF) on forward-facing scenes by …

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 …

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 …

We present NeX, a new approach to novel view synthesis based on enhancements of
multiplane image (MPI) that can reproduce next-level view-dependent effects--in real time …

We present a method for novel view synthesis from input images that are freely distributed
around a scene. Our method does not rely on a regular arrangement of input views, can …