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 …

Abstract Recently, the Segment Anything Model (SAM) emerged as a powerful vision
foundation model which is capable to segment anything in 2D images. This paper aims to …

Abstract Neural Radiance Fields (NeRFs) are emerging as a ubiquitous scene
representation that allows for novel view synthesis. Increasingly, NeRFs will be shareable …

The recent Gaussian Splatting achieves high-quality and real-time novel-view synthesis of
the 3D scenes. However, it is solely concentrated on the appearance and geometry …

3D Gaussian Splatting (3D-GS) has emerged as a significant advancement in the field of
Computer Graphics, offering explicit scene representation and novel view synthesis without …

Abstract The popularity of Neural Radiance Fields (NeRFs) for view synthesis has led to a
desire for NeRF editing tools. Here, we focus on inpainting regions in a view-consistent and …

We propose NeRFiller an approach that completes missing portions of a 3D capture via
generative 3D inpainting using off-the-shelf 2D visual generative models. Often parts of a …

Neural Radiance Field (NeRF) has achieved substantial progress in novel view synthesis
given multi-view images. Recently, some works have attempted to train a NeRF from a …

Towards holistic understanding of 3D scenes a general 3D segmentation method is needed
that can segment diverse objects without restrictions on object quantity or categories while …

This review thoroughly examines the role of semantically-aware Neural Radiance Fields
(NeRFs) in visual scene understanding, covering an analysis of over 250 scholarly papers. It …