no code implementations • 1 Apr 2024 • Armand Comas-Massagué, Di Qiu, Menglei Chai, Marcel Bühler, Amit Raj, Ruiqi Gao, Qiangeng Xu, Mark Matthews, Paulo Gotardo, Octavia Camps, Sergio Orts-Escolano, Thabo Beeler
We introduce a novel framework for 3D human avatar generation and personalization, leveraging text prompts to enhance user engagement and customization.
no code implementations • 5 Dec 2023 • Prafull Sharma, Varun Jampani, Yuanzhen Li, Xuhui Jia, Dmitry Lagun, Fredo Durand, William T. Freeman, Mark Matthews
We propose a method to control material attributes of objects like roughness, metallic, albedo, and transparency in real images.
no code implementations • 14 Mar 2023 • Axel Levy, Mark Matthews, Matan Sela, Gordon Wetzstein, Dmitry Lagun
Neural radiance fields enable novel-view synthesis and scene reconstruction with photorealistic quality from a few images, but require known and accurate camera poses.
no code implementations • CVPR 2023 • Cristina Vasconcelos, Cengiz Oztireli, Mark Matthews, Milad Hashemi, Kevin Swersky, Andrea Tagliasacchi
Neural fields have rapidly been adopted for representing 3D signals, but their application to more classical 2D image-processing has been relatively limited.
no code implementations • CVPR 2022 • Daniel Rebain, Mark Matthews, Kwang Moo Yi, Dmitry Lagun, Andrea Tagliasacchi
We present a method for learning a generative 3D model based on neural radiance fields, trained solely from data with only single views of each object.