Zobrazeno 1 - 10
of 197
pro vyhledávání: '"Goldman, Dan"'
In this pilot project, we teamed up with artists to develop new workflows for 2D animation while producing a short educational cartoon. We identified several workflows to streamline the animation process, bringing the artists' vision to the screen mo
Externí odkaz:
http://arxiv.org/abs/2405.11098
Autor:
Prabhu, Kira, Wu, Jane, Tsai, Lynn, Hedman, Peter, Goldman, Dan B, Poole, Ben, Broxton, Michael
This paper presents a novel approach to inpainting 3D regions of a scene, given masked multi-view images, by distilling a 2D diffusion model into a learned 3D scene representation (e.g. a NeRF). Unlike 3D generative methods that explicitly condition
Externí odkaz:
http://arxiv.org/abs/2312.03869
We present Readout Guidance, a method for controlling text-to-image diffusion models with learned signals. Readout Guidance uses readout heads, lightweight networks trained to extract signals from the features of a pre-trained, frozen diffusion model
Externí odkaz:
http://arxiv.org/abs/2312.02150
Autor:
Park, Keunhong, Sinha, Utkarsh, Hedman, Peter, Barron, Jonathan T., Bouaziz, Sofien, Goldman, Dan B, Martin-Brualla, Ricardo, Seitz, Steven M.
Neural Radiance Fields (NeRF) are able to reconstruct scenes with unprecedented fidelity, and various recent works have extended NeRF to handle dynamic scenes. A common approach to reconstruct such non-rigid scenes is through the use of a learned def
Externí odkaz:
http://arxiv.org/abs/2106.13228
Obtaining high-quality 3D reconstructions of room-scale scenes is of paramount importance for upcoming applications in AR or VR. These range from mixed reality applications for teleconferencing, virtual measuring, virtual room planing, to robotic app
Externí odkaz:
http://arxiv.org/abs/2104.04532
Autor:
Park, Keunhong, Sinha, Utkarsh, Barron, Jonathan T., Bouaziz, Sofien, Goldman, Dan B, Seitz, Steven M., Martin-Brualla, Ricardo
We present the first method capable of photorealistically reconstructing deformable scenes using photos/videos captured casually from mobile phones. Our approach augments neural radiance fields (NeRF) by optimizing an additional continuous volumetric
Externí odkaz:
http://arxiv.org/abs/2011.12948
Publikováno v:
European Conference on Computer Vision 2020
Accurate modeling of 3D objects exhibiting transparency, reflections and thin structures is an extremely challenging problem. Inspired by billboards and geometric proxies used in computer graphics, this paper proposes Generative Latent Textured Objec
Externí odkaz:
http://arxiv.org/abs/2008.04852
Autor:
Tewari, Ayush, Fried, Ohad, Thies, Justus, Sitzmann, Vincent, Lombardi, Stephen, Sunkavalli, Kalyan, Martin-Brualla, Ricardo, Simon, Tomas, Saragih, Jason, Nießner, Matthias, Pandey, Rohit, Fanello, Sean, Wetzstein, Gordon, Zhu, Jun-Yan, Theobalt, Christian, Agrawala, Maneesh, Shechtman, Eli, Goldman, Dan B, Zollhöfer, Michael
Efficient rendering of photo-realistic virtual worlds is a long standing effort of computer graphics. Modern graphics techniques have succeeded in synthesizing photo-realistic images from hand-crafted scene representations. However, the automatic gen
Externí odkaz:
http://arxiv.org/abs/2004.03805
Autor:
Fried, Ohad, Tewari, Ayush, Zollhöfer, Michael, Finkelstein, Adam, Shechtman, Eli, Goldman, Dan B, Genova, Kyle, Jin, Zeyu, Theobalt, Christian, Agrawala, Maneesh
Editing talking-head video to change the speech content or to remove filler words is challenging. We propose a novel method to edit talking-head video based on its transcript to produce a realistic output video in which the dialogue of the speaker ha
Externí odkaz:
http://arxiv.org/abs/1906.01524
Autor:
Meshry, Moustafa, Goldman, Dan B, Khamis, Sameh, Hoppe, Hugues, Pandey, Rohit, Snavely, Noah, Martin-Brualla, Ricardo
We explore total scene capture -- recording, modeling, and rerendering a scene under varying appearance such as season and time of day. Starting from internet photos of a tourist landmark, we apply traditional 3D reconstruction to register the photos
Externí odkaz:
http://arxiv.org/abs/1904.04290