no code implementations • 7 Dec 2023 • Nir Zabari, Aharon Azulay, Alexey Gorkor, Tavi Halperin, Ohad Fried
To tackle these issues, we present a novel image colorization framework that utilizes image diffusion techniques with granular text prompts.
1 code implementation • 16 Nov 2023 • Omri Avrahami, Amir Hertz, Yael Vinker, Moab Arar, Shlomi Fruchter, Ohad Fried, Daniel Cohen-Or, Dani Lischinski
Our quantitative analysis demonstrates that our method strikes a better balance between prompt alignment and identity consistency compared to the baseline methods, and these findings are reinforced by a user study.
4 code implementations • 1 Jun 2023 • Eran Levin, Ohad Fried
While current techniques enable user control over the degree of change in an image edit, the controllability is limited to global changes over an entire edited region.
1 code implementation • 25 May 2023 • Omri Avrahami, Kfir Aberman, Ohad Fried, Daniel Cohen-Or, Dani Lischinski
Text-to-image model personalization aims to introduce a user-provided concept to the model, allowing its synthesis in diverse contexts.
1 code implementation • 19 Mar 2023 • Sergey Sinitsa, Ohad Fried
Our method can detect images from a known generative model and enable us to establish relationships between fine-tuned generative models.
no code implementations • 2 Dec 2022 • Or Nachmias, Ohad Fried, Ariel Shamir
Understanding the 3D world from 2D images involves more than detection and segmentation of the objects within the scene.
1 code implementation • 1 Dec 2022 • Gil Knafo, Ohad Fried
This poses a problem, especially in the era of social media, as synthetic videos of speaking humans can be used to spread misinformation in a convincing manner.
2 code implementations • 29 Nov 2022 • Shimon Malnick, Shai Avidan, Ohad Fried
We propose an algorithm for taming Normalizing Flow models - changing the probability that the model will produce a specific image or image category.
1 code implementation • 27 Nov 2022 • Daniel Anderson, Ariel Shamir, Ohad Fried
Recent advances in deep learning techniques and applications have revolutionized artistic creation and manipulation in many domains (text, images, music); however, fonts have not yet been integrated with deep learning architectures in a manner that supports their multi-scale nature.
no code implementations • CVPR 2023 • Omri Avrahami, Thomas Hayes, Oran Gafni, Sonal Gupta, Yaniv Taigman, Devi Parikh, Dani Lischinski, Ohad Fried, Xi Yin
Due to lack of large-scale datasets that have a detailed textual description for each region in the image, we choose to leverage the current large-scale text-to-image datasets and base our approach on a novel CLIP-based spatio-textual representation, and show its effectiveness on two state-of-the-art diffusion models: pixel-based and latent-based.
1 code implementation • 24 Nov 2022 • Daniel Arkushin, Bar Cohen, Shmuel Peleg, Ohad Fried
Combined with the latest ReID models, our method achieves new SOTA results on the PRCC, LTCC, CCVID, LaST and VC-Clothes benchmarks and the proposed 42Street dataset.
Ranked #1 on Person Re-Identification on PRCC
1 code implementation • CVPR 2023 • Rotem Shalev-Arkushin, Amit Moryossef, Ohad Fried
Additionally, we offer a new distance measurement that considers missing keypoints, to measure the distance between pose sequences using DTW-MJE.
1 code implementation • 6 Jun 2022 • Omri Avrahami, Ohad Fried, Dani Lischinski
Our solution leverages a recent text-to-image Latent Diffusion Model (LDM), which speeds up diffusion by operating in a lower-dimensional latent space.
1 code implementation • 30 Mar 2022 • David Dadon, Ohad Fried, Yacov Hel-Or
We present depth distribution neural radiance field (DDNeRF), a new method that significantly increases sampling efficiency along rays during training while achieving superior results for a given sampling budget.
no code implementations • CVPR 2022 • Ayush Tewari, Mallikarjun B R, Xingang Pan, Ohad Fried, Maneesh Agrawala, Christian Theobalt
Our model can disentangle the geometry and appearance variations in the scene, i. e., we can independently sample from the geometry and appearance spaces of the generative model.
1 code implementation • 28 Mar 2022 • Asaf Karnieli, Ohad Fried, Yacov Hel-Or
We show that the self and cast shadows not only do not disturb 3D reconstruction, but can be used alone, as a strong learning signal, to recover the depth map and surface normals.
1 code implementation • CVPR 2022 • Omri Avrahami, Dani Lischinski, Ohad Fried
Natural language offers a highly intuitive interface for image editing.
text-guided-image-editing Zero-Shot Text-to-Image Generation
1 code implementation • 7 Jun 2021 • Omri Avrahami, Dani Lischinski, Ohad Fried
In the second stage, we merge the rooted models by averaging their weights and fine-tuning them for each specific domain, using only data generated by the original trained models.
no code implementations • 19 May 2021 • Tavi Halperin, Hanit Hakim, Orestis Vantzos, Gershon Hochman, Netai Benaim, Lior Sassy, Michael Kupchik, Ofir Bibi, Ohad Fried
We present an algorithm for producing a seamless animated loop from a single image.
no code implementations • 21 Nov 2020 • Xinwei Yao, Ohad Fried, Kayvon Fatahalian, Maneesh Agrawala
We present a text-based tool for editing talking-head video that enables an iterative editing workflow.
no code implementations • 8 Apr 2020 • Ayush Tewari, Ohad Fried, Justus Thies, Vincent Sitzmann, Stephen Lombardi, Kalyan Sunkavalli, Ricardo Martin-Brualla, Tomas Simon, Jason Saragih, Matthias Nießner, Rohit Pandey, Sean Fanello, Gordon Wetzstein, Jun-Yan Zhu, Christian Theobalt, Maneesh Agrawala, Eli Shechtman, Dan B. Goldman, Michael Zollhöfer
Neural rendering is a new and rapidly emerging field that combines generative machine learning techniques with physical knowledge from computer graphics, e. g., by the integration of differentiable rendering into network training.
2 code implementations • ECCV 2020 • Roy Or-El, Soumyadip Sengupta, Ohad Fried, Eli Shechtman, Ira Kemelmacher-Shlizerman
Most existing aging methods are limited to changing the texture, overlooking transformations in head shape that occur during the human aging and growth process.
1 code implementation • 4 Jun 2019 • Ohad Fried, Ayush Tewari, Michael Zollhöfer, Adam Finkelstein, Eli Shechtman, Dan B. Goldman, Kyle Genova, Zeyu Jin, Christian Theobalt, Maneesh Agrawala
To edit a video, the user has to only edit the transcript, and an optimization strategy then chooses segments of the input corpus as base material.
no code implementations • CVPR 2015 • Ohad Fried, Eli Shechtman, Dan B. Goldman, Adam Finkelstein
We propose a new computer vision task we call "distractor prediction."