Abstract Neural Radiance Field training can be accelerated through the use of grid-based
representations in NeRF's learned map** from spatial coordinates to colors and …

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 …

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 propose a novel geometric and photometric 3D map** pipeline for accurate and real-
time scene reconstruction from casually taken monocular images. To achieve this, we …

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 …

Recent significant advances in text-to-image models unlock the possibility of training vision
systems using synthetic images potentially overcoming the difficulty of collecting curated …

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 …

The proliferation of technologies, such as extended reality (XR), has increased the demand
for high-quality three-dimensional (3D) graphical representations. Industrial 3D applications …

Abstract Neural Radiance Field (NeRF) has emerged as a powerful paradigm for scene
representation, offering high-fidelity renderings and reconstructions from a set of sparse and …

Probabilistic diffusion models have achieved state-of-the-art results for image synthesis,
inpainting, and text-to-image tasks. However, they are still in the early stages of generating …