Search Results for author: Ohad Fried

Found 24 papers, 16 papers with code

Diffusing Colors: Image Colorization with Text Guided Diffusion

no code implementations7 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.

Colorization Image Colorization

The Chosen One: Consistent Characters in Text-to-Image Diffusion Models

1 code implementation16 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.

Consistent Character Generation Story Visualization

Differential Diffusion: Giving Each Pixel Its Strength

1 code implementation1 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.

Image Generation Text-based Image Editing

Break-A-Scene: Extracting Multiple Concepts from a Single Image

1 code implementation25 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.

Complex Scene Breaking and Synthesis

Deep Image Fingerprint: Towards Low Budget Synthetic Image Detection and Model Lineage Analysis

1 code implementation19 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.

Inductive Bias Synthetic Image Detection

Prediction of Scene Plausibility

no code implementations2 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.

Scene Understanding

FakeOut: Leveraging Out-of-domain Self-supervision for Multi-modal Video Deepfake Detection

1 code implementation1 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.

DeepFake Detection Face Swapping +1

Taming Normalizing Flows

2 code implementations29 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.

Neural Font Rendering

1 code implementation27 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.

SpaText: Spatio-Textual Representation for Controllable Image Generation

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.

Text-to-Image Generation

GEFF: Improving Any Clothes-Changing Person ReID Model using Gallery Enrichment with Face Features

1 code implementation24 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.

Person Re-Identification

Ham2Pose: Animating Sign Language Notation into Pose Sequences

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.

Dynamic Time Warping

Blended Latent Diffusion

1 code implementation6 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.

Image Inpainting text-guided-image-editing +1

DDNeRF: Depth Distribution Neural Radiance Fields

1 code implementation30 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.

Novel View Synthesis

Disentangled3D: Learning a 3D Generative Model with Disentangled Geometry and Appearance from Monocular Images

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.


DeepShadow: Neural Shape from Shadow

1 code implementation28 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.

3D Reconstruction

GAN Cocktail: mixing GANs without dataset access

1 code implementation7 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.

Transfer Learning

Iterative Text-based Editing of Talking-heads Using Neural Retargeting

no code implementations21 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.

State of the Art on Neural Rendering

no code implementations8 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.

BIG-bench Machine Learning Image Generation +2

Lifespan Age Transformation Synthesis

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.

Face Age Editing Generative Adversarial Network +5

Text-based Editing of Talking-head Video

1 code implementation4 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.

Face Model Sentence +3

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