no code implementations • 21 Mar 2024 • Narek Tumanyan, Assaf Singer, Shai Bagon, Tali Dekel
Specifically, our framework simultaneously adopts DINO's features to fit to the motion observations of the test video, while training a tracker that directly leverages the refined features.
no code implementations • 23 Jan 2024 • Omer Bar-Tal, Hila Chefer, Omer Tov, Charles Herrmann, Roni Paiss, Shiran Zada, Ariel Ephrat, Junhwa Hur, Guanghui Liu, Amit Raj, Yuanzhen Li, Michael Rubinstein, Tomer Michaeli, Oliver Wang, Deqing Sun, Tali Dekel, Inbar Mosseri
We introduce Lumiere -- a text-to-video diffusion model designed for synthesizing videos that portray realistic, diverse and coherent motion -- a pivotal challenge in video synthesis.
Ranked #6 on Text-to-Video Generation on UCF-101
no code implementations • 28 Nov 2023 • Danah Yatim, Rafail Fridman, Omer Bar-Tal, Yoni Kasten, Tali Dekel
This loss guides the generation process to preserve the overall motion of the input video while complying with the target object in terms of shape and fine-grained motion traits.
no code implementations • 20 Nov 2023 • Narek Tumanyan, Omer Bar-Tal, Shir Amir, Shai Bagon, Tali Dekel
Specifically, our goal is to generate an image in which objects in a source structure image are "painted" with the visual appearance of their semantically related objects in a target appearance image.
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.
1 code implementation • 19 Jul 2023 • Michal Geyer, Omer Bar-Tal, Shai Bagon, Tali Dekel
In this work, we present a framework that harnesses the power of a text-to-image diffusion model for the task of text-driven video editing.
1 code implementation • NeurIPS 2023 • Stephanie Fu, Netanel Tamir, Shobhita Sundaram, Lucy Chai, Richard Zhang, Tali Dekel, Phillip Isola
Furthermore, our metric outperforms both prior learned metrics and recent large vision models on these tasks.
no code implementations • ICCV 2023 • Roni Paiss, Ariel Ephrat, Omer Tov, Shiran Zada, Inbar Mosseri, Michal Irani, Tali Dekel
Our counting loss is deployed over automatically-created counterfactual examples, each consisting of an image and a caption containing an incorrect object count.
2 code implementations • 16 Feb 2023 • Omer Bar-Tal, Lior Yariv, Yaron Lipman, Tali Dekel
In this work, we present MultiDiffusion, a unified framework that enables versatile and controllable image generation, using a pre-trained text-to-image diffusion model, without any further training or finetuning.
no code implementations • CVPR 2023 • Dolev Ofri-Amar, Michal Geyer, Yoni Kasten, Tali Dekel
We present Neural Congealing -- a zero-shot self-supervised framework for detecting and jointly aligning semantically-common content across a given set of images.
no code implementations • NeurIPS 2023 • Rafail Fridman, Amit Abecasis, Yoni Kasten, Tali Dekel
We present a method for text-driven perpetual view generation -- synthesizing long-term videos of various scenes solely, given an input text prompt describing the scene and camera poses.
3 code implementations • CVPR 2023 • Narek Tumanyan, Michal Geyer, Shai Bagon, Tali Dekel
Large-scale text-to-image generative models have been a revolutionary breakthrough in the evolution of generative AI, allowing us to synthesize diverse images that convey highly complex visual concepts.
Ranked #10 on Text-based Image Editing on PIE-Bench
no code implementations • CVPR 2023 • Bahjat Kawar, Shiran Zada, Oran Lang, Omer Tov, Huiwen Chang, Tali Dekel, Inbar Mosseri, Michal Irani
In this paper we demonstrate, for the very first time, the ability to apply complex (e. g., non-rigid) text-guided semantic edits to a single real image.
no code implementations • 11 May 2022 • Niv Haim, Ben Feinstein, Niv Granot, Assaf Shocher, Shai Bagon, Tali Dekel, Michal Irani
GANs are able to perform generation and manipulation tasks, trained on a single video.
1 code implementation • 5 Apr 2022 • Omer Bar-Tal, Dolev Ofri-Amar, Rafail Fridman, Yoni Kasten, Tali Dekel
Given an input image or video and a target text prompt, our goal is to edit the appearance of existing objects (e. g., object's texture) or augment the scene with visual effects (e. g., smoke, fire) in a semantically meaningful manner.
2 code implementations • 24 Feb 2022 • Ron Mokady, Michal Yarom, Omer Tov, Oran Lang, Daniel Cohen-Or, Tali Dekel, Michal Irani, Inbar Mosseri
To meet these challenges, we proposed a StyleGAN-based self-distillation approach, which consists of two main components: (i) A generative-based self-filtering of the dataset to eliminate outlier images, in order to generate an adequate training set, and (ii) Perceptual clustering of the generated images to detect the inherent data modalities, which are then employed to improve StyleGAN's "truncation trick" in the image synthesis process.
1 code implementation • CVPR 2022 • Narek Tumanyan, Omer Bar-Tal, Shai Bagon, Tali Dekel
Specifically, our goal is to generate an image in which objects in a source structure image are "painted" with the visual appearance of their semantically related objects in a target appearance image.
1 code implementation • 10 Dec 2021 • Shir Amir, Yossi Gandelsman, Shai Bagon, Tali Dekel
To distill the power of ViT features from convoluted design choices, we restrict ourselves to lightweight zero-shot methodologies (e. g., binning and clustering) applied directly to the features.
Ranked #6 on Feature Upsampling on ImageNet
2 code implementations • 23 Sep 2021 • Yoni Kasten, Dolev Ofri, Oliver Wang, Tali Dekel
We present a method that decomposes, or "unwraps", an input video into a set of layered 2D atlases, each providing a unified representation of the appearance of an object (or background) over the video.
no code implementations • 17 Sep 2021 • Niv Haim, Ben Feinstein, Niv Granot, Assaf Shocher, Shai Bagon, Tali Dekel, Michal Irani
GANs are able to perform generation and manipulation tasks, trained on a single video.
no code implementations • 2 Aug 2021 • Zhoutong Zhang, Forrester Cole, Richard Tucker, William T. Freeman, Tali Dekel
We present a method to estimate depth of a dynamic scene, containing arbitrary moving objects, from an ordinary video captured with a moving camera.
no code implementations • CVPR 2021 • Erika Lu, Forrester Cole, Tali Dekel, Andrew Zisserman, William T. Freeman, Michael Rubinstein
We show results on real-world videos containing interactions between different types of subjects (cars, animals, people) and complex effects, ranging from semi-transparent elements such as smoke and reflections, to fully opaque effects such as objects attached to the subject.
1 code implementation • 16 Sep 2020 • Erika Lu, Forrester Cole, Tali Dekel, Weidi Xie, Andrew Zisserman, David Salesin, William T. Freeman, Michael Rubinstein
We present a method for retiming people in an ordinary, natural video -- manipulating and editing the time in which different motions of individuals in the video occur.
1 code implementation • CVPR 2020 • Sagie Benaim, Ariel Ephrat, Oran Lang, Inbar Mosseri, William T. Freeman, Michael Rubinstein, Michal Irani, Tali Dekel
We demonstrate how those learned features can boost the performance of self-supervised action recognition, and can be used for video retrieval.
2 code implementations • CVPR 2020 • Assaf Shocher, Yossi Gandelsman, Inbar Mosseri, Michal Yarom, Michal Irani, William T. Freeman, Tali Dekel
We demonstrate that our model results in a versatile and flexible framework that can be used in various classic and novel image generation tasks.
3 code implementations • CVPR 2019 • Tae-Hyun Oh, Tali Dekel, Changil Kim, Inbar Mosseri, William T. Freeman, Michael Rubinstein, Wojciech Matusik
How much can we infer about a person's looks from the way they speak?
44 code implementations • ICCV 2019 • Tamar Rott Shaham, Tali Dekel, Tomer Michaeli
We introduce SinGAN, an unconditional generative model that can be learned from a single natural image.
no code implementations • CVPR 2019 • Zhengqi Li, Tali Dekel, Forrester Cole, Richard Tucker, Noah Snavely, Ce Liu, William T. Freeman
We present a method for predicting dense depth in scenarios where both a monocular camera and people in the scene are freely moving.
no code implementations • 14 Sep 2018 • Xiuming Zhang, Tali Dekel, Tianfan Xue, Andrew Owens, Qiurui He, Jiajun Wu, Stefanie Mueller, William T. Freeman
We present a system that allows users to visualize complex human motion via 3D motion sculptures---a representation that conveys the 3D structure swept by a human body as it moves through space.
no code implementations • CVPR 2018 • Tali Dekel, Chuang Gan, Dilip Krishnan, Ce Liu, William T. Freeman
We study the problem of reconstructing an image from information stored at contour locations.
no code implementations • CVPR 2018 • Tal Tlusty, Tomer Michaeli, Tali Dekel, Lihi Zelnik-Manor
We present an algorithm for modifying small non-local variations between repeating structures and patterns in multiple images of the same scene.
5 code implementations • 10 Apr 2018 • Ariel Ephrat, Inbar Mosseri, Oran Lang, Tali Dekel, Kevin Wilson, Avinatan Hassidim, William T. Freeman, Michael Rubinstein
Solving this task using only audio as input is extremely challenging and does not provide an association of the separated speech signals with speakers in the video.
no code implementations • 21 Dec 2017 • Tali Dekel, Chuang Gan, Dilip Krishnan, Ce Liu, William T. Freeman
We study the problem of reconstructing an image from information stored at contour locations.
no code implementations • CVPR 2017 • Tali Dekel, Michael Rubinstein, Ce Liu, William T. Freeman
Since such an attack relies on the consistency of watermarks across image collection, we explore and evaluate how it is affected by various types of inconsistencies in the watermark embedding that could potentially be used to make watermarking more secured.
no code implementations • 6 Sep 2016 • Shaul Oron, Tali Dekel, Tianfan Xue, William T. Freeman, Shai Avidan
We propose a novel method for template matching in unconstrained environments.
no code implementations • CVPR 2015 • Tali Dekel, Shaul Oron, Michael Rubinstein, Shai Avidan, William T. Freeman
We propose a novel method for template matching in unconstrained environments.