no code implementations • 29 May 2025 • Xiaojuan Wang, Aleksander Holynski, Brian Curless, Ira Kemelmacher, Steve Seitz
We present a keyframe-based framework for generating music-synchronized, choreography aware animal dance videos.
no code implementations • 18 Mar 2025 • Stanislaw Szymanowicz, Jason Y. Zhang, Pratul Srinivasan, Ruiqi Gao, Arthur Brussee, Aleksander Holynski, Ricardo Martin-Brualla, Jonathan T. Barron, Philipp Henzler
We present a latent diffusion model for fast feed-forward 3D scene generation.
no code implementations • CVPR 2025 • Qianqian Wang, Yifei Zhang, Aleksander Holynski, Alexei A. Efros, Angjoo Kanazawa
We present a unified framework capable of solving a broad range of 3D tasks.
no code implementations • CVPR 2025 • Chao Feng, Ziyang Chen, Aleksander Holynski, Alexei A. Efros, Andrew Owens
We show that the GPS tags contained in photo metadata provide a useful control signal for image generation.
no code implementations • CVPR 2025 • Zhengqi Li, Richard Tucker, Forrester Cole, Qianqian Wang, Linyi Jin, Vickie Ye, Angjoo Kanazawa, Aleksander Holynski, Noah Snavely
We present a system that allows for accurate, fast, and robust estimation of camera parameters and depth maps from casual monocular videos of dynamic scenes.
no code implementations • CVPR 2025 • Linyi Jin, Richard Tucker, Zhengqi Li, David Fouhey, Noah Snavely, Aleksander Holynski
Learning to understand dynamic 3D scenes from imagery is crucial for applications ranging from robotics to scene reconstruction.
no code implementations • CVPR 2025 • Alex Trevithick, Roni Paiss, Philipp Henzler, Dor Verbin, Rundi Wu, Hadi AlZayer, Ruiqi Gao, Ben Poole, Jonathan T. Barron, Aleksander Holynski, Ravi Ramamoorthi, Pratul P. Srinivasan
Novel-view synthesis techniques achieve impressive results for static scenes but struggle when faced with the inconsistencies inherent to casual capture settings: varying illumination, scene motion, and other unintended effects that are difficult to model explicitly.
1 code implementation • 5 Dec 2024 • Zhengqi Li, Richard Tucker, Forrester Cole, Qianqian Wang, Linyi Jin, Vickie Ye, Angjoo Kanazawa, Aleksander Holynski, Noah Snavely
We present a system that allows for accurate, fast, and robust estimation of camera parameters and depth maps from casual monocular videos of dynamic scenes.
no code implementations • CVPR 2025 • Rundi Wu, Ruiqi Gao, Ben Poole, Alex Trevithick, Changxi Zheng, Jonathan T. Barron, Aleksander Holynski
We present CAT4D, a method for creating 4D (dynamic 3D) scenes from monocular video.
no code implementations • 17 Oct 2024 • Jingwei Ma, Erika Lu, Roni Paiss, Shiran Zada, Aleksander Holynski, Tali Dekel, Brian Curless, Michael Rubinstein, Forrester Cole
Panoramic image stitching provides a unified, wide-angle view of a scene that extends beyond the camera's field of view.
no code implementations • 27 Aug 2024 • Xiaojuan Wang, Boyang Zhou, Brian Curless, Ira Kemelmacher-Shlizerman, Aleksander Holynski, Steven M. Seitz
We adapt a pretrained large-scale image-to-video diffusion model (originally trained to generate videos moving forward in time from a single input image) for key frame interpolation, i. e., to produce a video in between two input frames.
no code implementations • 20 Jul 2024 • Ioannis Siglidis, Aleksander Holynski, Alexei A. Efros, Mathieu Aubry, Shiry Ginosar
Concretely, we show that after finetuning conditional diffusion models to synthesize images from a specific dataset, we can use these models to define a typicality measure on that dataset.
no code implementations • 13 Jun 2024 • David McAllister, Songwei Ge, Jia-Bin Huang, David W. Jacobs, Alexei A. Efros, Aleksander Holynski, Angjoo Kanazawa
We compare our method to existing approaches for score distillation sampling and show that it can produce high-frequency details with realistic colors.
no code implementations • CVPR 2024 • Meng-Li Shih, Wei-Chiu Ma, Aleksander Holynski, Forrester Cole, Brian L. Curless, Janne Kontkanen
We propose ExtraNeRF, a novel method for extrapolating the range of views handled by a Neural Radiance Field (NeRF).
no code implementations • 16 May 2024 • Ruiqi Gao, Aleksander Holynski, Philipp Henzler, Arthur Brussee, Ricardo Martin-Brualla, Pratul Srinivasan, Jonathan T. Barron, Ben Poole
Advances in 3D reconstruction have enabled high-quality 3D capture, but require a user to collect hundreds to thousands of images to create a 3D scene.
no code implementations • 13 May 2024 • Yifan Wang, Aleksander Holynski, Brian L. Curless, Steven M. Seitz
We present Infinite Texture, a method for generating arbitrarily large texture images from a text prompt.
no code implementations • 26 Feb 2024 • Dave Epstein, Ben Poole, Ben Mildenhall, Alexei A. Efros, Aleksander Holynski
We introduce a method to generate 3D scenes that are disentangled into their component objects.
no code implementations • CVPR 2024 • Rundi Wu, Ben Mildenhall, Philipp Henzler, Keunhong Park, Ruiqi Gao, Daniel Watson, Pratul P. Srinivasan, Dor Verbin, Jonathan T. Barron, Ben Poole, Aleksander Holynski
3D reconstruction methods such as Neural Radiance Fields (NeRFs) excel at rendering photorealistic novel views of complex scenes.
no code implementations • CVPR 2024 • Grace Luo, Trevor Darrell, Oliver Wang, Dan B Goldman, Aleksander Holynski
We present Readout Guidance, a method for controlling text-to-image diffusion models with learned signals.
no code implementations • CVPR 2024 • Xiaojuan Wang, Janne Kontkanen, Brian Curless, Steve Seitz, Ira Kemelmacher, Ben Mildenhall, Pratul Srinivasan, Dor Verbin, Aleksander Holynski
We present a method that uses a text-to-image model to generate consistent content across multiple image scales, enabling extreme semantic zooms into a scene, e. g., ranging from a wide-angle landscape view of a forest to a macro shot of an insect sitting on one of the tree branches.
no code implementations • 11 Oct 2023 • Ryan Po, Wang Yifan, Vladislav Golyanik, Kfir Aberman, Jonathan T. Barron, Amit H. Bermano, Eric Ryan Chan, Tali Dekel, Aleksander Holynski, Angjoo Kanazawa, C. Karen Liu, Lingjie Liu, Ben Mildenhall, Matthias Nießner, Björn Ommer, Christian Theobalt, Peter Wonka, Gordon Wetzstein
The field of visual computing is rapidly advancing due to the emergence of generative artificial intelligence (AI), which unlocks unprecedented capabilities for the generation, editing, and reconstruction of images, videos, and 3D scenes.
no code implementations • 28 Sep 2023 • Luming Tang, Nataniel Ruiz, Qinghao Chu, Yuanzhen Li, Aleksander Holynski, David E. Jacobs, Bharath Hariharan, Yael Pritch, Neal Wadhwa, Kfir Aberman, Michael Rubinstein
Once personalized, RealFill is able to complete a target image with visually compelling contents that are faithful to the original scene.
1 code implementation • CVPR 2024 • Zhengqi Li, Richard Tucker, Noah Snavely, Aleksander Holynski
We present an approach to modeling an image-space prior on scene motion.
1 code implementation • ICCV 2023 • Qianqian Wang, Yen-Yu Chang, Ruojin Cai, Zhengqi Li, Bharath Hariharan, Aleksander Holynski, Noah Snavely
We present a new test-time optimization method for estimating dense and long-range motion from a video sequence.
1 code implementation • NeurIPS 2023 • Dave Epstein, Allan Jabri, Ben Poole, Alexei A. Efros, Aleksander Holynski
However, many aspects of an image are difficult or impossible to convey through text.
no code implementations • NeurIPS 2023 • Grace Luo, Lisa Dunlap, Dong Huk Park, Aleksander Holynski, Trevor Darrell
We propose Diffusion Hyperfeatures, a framework for consolidating multi-scale and multi-timestep feature maps into per-pixel feature descriptors that can be used for downstream tasks.
1 code implementation • ICCV 2023 • Frederik Warburg, Ethan Weber, Matthew Tancik, Aleksander Holynski, Angjoo Kanazawa
Casually captured Neural Radiance Fields (NeRFs) suffer from artifacts such as floaters or flawed geometry when rendered outside the camera trajectory.
no code implementations • 12 Apr 2023 • Johanna Karras, Aleksander Holynski, Ting-Chun Wang, Ira Kemelmacher-Shlizerman
We fine-tune on a collection of fashion videos from the UBC Fashion dataset.
1 code implementation • ICCV 2023 • Ayaan Haque, Matthew Tancik, Alexei A. Efros, Aleksander Holynski, Angjoo Kanazawa
We propose a method for editing NeRF scenes with text-instructions.
no code implementations • ICCV 2023 • Johanna Karras, Aleksander Holynski, Ting-Chun Wang, Ira Kemelmacher-Shlizerman
We fine-tune on a collection of fashion videos from the UBC Fashion dataset.
6 code implementations • CVPR 2023 • Tim Brooks, Aleksander Holynski, Alexei A. Efros
We propose a method for editing images from human instructions: given an input image and a written instruction that tells the model what to do, our model follows these instructions to edit the image.
Ranked #7 on
Image Editing
on ImgEdit-Data
no code implementations • CVPR 2023 • Yifan Wang, Aleksander Holynski, Xiuming Zhang, Xuaner Zhang
Our method only requires the user to capture a selfie video outdoors, rotating in place, and uses the varying angles between the sun and the face as guidance in joint reconstruction of facial geometry, reflectance, camera pose, and lighting parameters.
no code implementations • CVPR 2021 • Aleksander Holynski, Brian Curless, Steven M. Seitz, Richard Szeliski
In this paper, we demonstrate a fully automatic method for converting a still image into a realistic animated looping video.
no code implementations • 27 Aug 2020 • Aleksander Holynski, David Geraghty, Jan-Michael Frahm, Chris Sweeney, Richard Szeliski
Low-frequency long-range errors (drift) are an endemic problem in 3D structure from motion, and can often hamper reasonable reconstructions of the scene.
no code implementations • CVPR 2020 • Jeong Joon Park, Aleksander Holynski, Steve Seitz
We address the dual problems of novel view synthesis and environment reconstruction from hand-held RGBD sensors.
no code implementations • 8 Jun 2019 • Chris Sweeney, Aleksander Holynski, Brian Curless, Steve M Seitz
We present a novel Structure from Motion pipeline that is capable of reconstructing accurate camera poses for panorama-style video capture without prior camera intrinsic calibration.