The emergence of 3D Gaussian splatting (3DGS) has greatly accelerated rendering in novel
view synthesis. Unlike neural implicit representations like neural radiance fields (NeRFs) …

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 …

Representing and rendering dynamic scenes has been an important but challenging task.
Especially to accurately model complex motions high efficiency is usually hard to guarantee …

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 …

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

We present GaussianAvatar an efficient approach to creating realistic human avatars with
dynamic 3D appearances from a single video. We start by introducing animatable 3D …

We present Tensor4D, an efficient yet effective approach to dynamic scene modeling. The
key of our solution is an efficient 4D tensor decomposition method so that the dynamic scene …

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 …