Humans describe the physical world using natural language to refer to specific 3D locations
based on a vast range of properties: visual appearance, semantics, abstract associations, or …

Emerging neural radiance fields (NeRF) are a promising scene representation for computer
graphics, enabling high-quality 3D reconstruction and novel view synthesis from image …

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 Neural Radiance Fields (NeRFs) have emerged as a popular approach for novel
view synthesis. While NeRFs are quickly being adapted for a wider set of applications …

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 …

We present Neural Feature Fusion Fields (N3F),\a method that improves dense 2D image
feature extractors when the latter are applied to the analysis of multiple images …

Abstract 3D scene representations have gained immense popularity in recent years.
Methods that use Neural Radiance fields are versatile for traditional tasks such as novel …

Grouping is inherently ambiguous due to the multiple levels of granularity in which one can
decompose a scene---should the wheels of an excavator be considered separate or part of …

Radiance Fields (RF) are popular to represent casually-captured scenes for new view
synthesis and several applications beyond it. Mixed reality on personal spaces needs …

In this paper, we study the problem of 3D scene geometry decomposition and manipulation
from 2D views. By leveraging the recent implicit neural representation techniques …