Search Results for author: Moab Arar

Found 11 papers, 5 papers with code

AnyLens: A Generative Diffusion Model with Any Rendering Lens

no code implementations29 Nov 2023 Andrey Voynov, Amir Hertz, Moab Arar, Shlomi Fruchter, Daniel Cohen-Or

State-of-the-art diffusion models can generate highly realistic images based on various conditioning like text, segmentation, and depth.

Text Segmentation

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

Domain-Agnostic Tuning-Encoder for Fast Personalization of Text-To-Image Models

no code implementations13 Jul 2023 Moab Arar, Rinon Gal, Yuval Atzmon, Gal Chechik, Daniel Cohen-Or, Ariel Shamir, Amit H. Bermano

Text-to-image (T2I) personalization allows users to guide the creative image generation process by combining their own visual concepts in natural language prompts.

Image Generation

Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models

no code implementations23 Feb 2023 Rinon Gal, Moab Arar, Yuval Atzmon, Amit H. Bermano, Gal Chechik, Daniel Cohen-Or

Specifically, we employ two components: First, an encoder that takes as an input a single image of a target concept from a given domain, e. g. a specific face, and learns to map it into a word-embedding representing the concept.

Novel Concepts

Single Motion Diffusion

1 code implementation12 Feb 2023 Sigal Raab, Inbal Leibovitch, Guy Tevet, Moab Arar, Amit H. Bermano, Daniel Cohen-Or

We harness the power of diffusion models and present a denoising network explicitly designed for the task of learning from a single input motion.

Denoising Style Transfer

InAugment: Improving Classifiers via Internal Augmentation

1 code implementation8 Apr 2021 Moab Arar, Ariel Shamir, Amit Bermano

Image augmentation techniques apply transformation functions such as rotation, shearing, or color distortion on an input image.

Image Augmentation

Focus-and-Expand: Training Guidance Through Gradual Manipulation of Input Features

no code implementations15 Jul 2020 Moab Arar, Noa Fish, Dani Daniel, Evgeny Tenetov, Ariel Shamir, Amit Bermano

Drawing inspiration from Parameter Continuation methods, we propose steering the training process to consider specific features in the input more than others, through gradual shifts in the input domain.

Image Classification

Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation

1 code implementation CVPR 2020 Moab Arar, Yiftach Ginger, Dov Danon, Ilya Leizerson, Amit Bermano, Daniel Cohen-Or

In this work, we bypass the difficulties of developing cross-modality similarity measures, by training an image-to-image translation network on the two input modalities.

Autonomous Driving Image Registration +2

Image Resizing by Reconstruction from Deep Features

no code implementations17 Apr 2019 Moab Arar, Dov Danon, Daniel Cohen-Or, Ariel Shamir

In this paper we perform image resizing in feature space where the deep layers of a neural network contain rich important semantic information.

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