Neural Radiance Field (NeRF) has recently become a significant development in the field of
Computer Vision, allowing for implicit, neural network-based scene representation and …

Large language models (LLMs) are increasingly deployed as the backend for various
applications, including code completion tools and AI-powered search engines. Unlike …

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 …

In this work, we investigate the problem of creating high-fidelity 3D content from only a single
image. This is inherently challenging: it essentially involves estimating the underlying 3D …

Abstract We present Stable Video 3D (SV3D)—a latent video diffusion model for high-
resolution, image-to-multi-view generation of orbital videos around a 3D object. Recent …

Novel view synthesis with sparse inputs is a challenging problem for neural radiance fields
(NeRF). Recent efforts alleviate this challenge by introducing external supervision, such as …

Abstract Neural Radiance Field (NeRF) significantly degrades when only a limited number
of views are available. To complement the lack of 3D information, depth-based models, such …

Abstract Neural Radiance Fields (NeRFs) have demonstrated remarkable potential in
capturing complex 3D scenes with high fidelity. However one persistent challenge that …

Abstract 3D reconstruction methods such as Neural Radiance Fields (NeRFs) excel at
rendering photorealistic novel views of complex scenes. However recovering a high-quality …

We introduce MVSplat, an efficient model that, given sparse multi-view images as input,
predicts clean feed-forward 3D Gaussians. To accurately localize the Gaussian centers, we …