no code implementations • 4 Sep 2023 • Ryan Po, Zhengyang Dong, Alexander W. Bergman, Gordon Wetzstein
Neural radiance fields (NeRFs) have emerged as an effective method for novel-view synthesis and 3D scene reconstruction.
no code implementations • 11 Jul 2023 • Yinghao Xu, Wang Yifan, Alexander W. Bergman, Menglei Chai, Bolei Zhou, Gordon Wetzstein
These layers are rendered using alpha compositing with fast differentiable rasterization, and they can be interpreted as a volumetric representation that allocates its capacity to a manifold of finite thickness around the template.
no code implementations • 10 Jul 2023 • Alexander W. Bergman, Wang Yifan, Gordon Wetzstein
Recent work on text-guided 3D object generation has shown great promise in addressing these needs.
no code implementations • ICCV 2023 • Eric R. Chan, Koki Nagano, Matthew A. Chan, Alexander W. Bergman, Jeong Joon Park, Axel Levy, Miika Aittala, Shalini De Mello, Tero Karras, Gordon Wetzstein
We present a diffusion-based model for 3D-aware generative novel view synthesis from as few as a single input image.
no code implementations • 7 Mar 2023 • Cindy M. Nguyen, Eric R. Chan, Alexander W. Bergman, Gordon Wetzstein
Capturing images is a key part of automation for high-level tasks such as scene text recognition.
no code implementations • 28 Jun 2022 • Alexander W. Bergman, Petr Kellnhofer, Wang Yifan, Eric R. Chan, David B. Lindell, Gordon Wetzstein
Unsupervised learning of 3D-aware generative adversarial networks (GANs) using only collections of single-view 2D photographs has very recently made much progress.
no code implementations • NeurIPS 2021 • Alexander W. Bergman, Petr Kellnhofer, Gordon Wetzstein
Inspired by neural variants of image-based rendering, we develop a new neural rendering approach with the goal of quickly learning a high-quality representation which can also be rendered in real-time.
no code implementations • 25 Mar 2021 • Daniel Martin, Ana Serrano, Alexander W. Bergman, Gordon Wetzstein, Belen Masia
Generative adversarial approaches could alleviate this challenge by generating a large number of possible scanpaths for unseen images.
24 code implementations • NeurIPS 2020 • Vincent Sitzmann, Julien N. P. Martel, Alexander W. Bergman, David B. Lindell, Gordon Wetzstein
However, current network architectures for such implicit neural representations are incapable of modeling signals with fine detail, and fail to represent a signal's spatial and temporal derivatives, despite the fact that these are essential to many physical signals defined implicitly as the solution to partial differential equations.