no code implementations • 21 Mar 2024 • Yuval Alaluf, Elad Richardson, Sergey Tulyakov, Kfir Aberman, Daniel Cohen-Or
To effectively recognize a variety of user-specific concepts, we augment the VLM with external concept heads that function as toggles for the model, enabling the VLM to identify the presence of specific target concepts in a given image.
no code implementations • 21 Nov 2023 • Rinon Gal, Yael Vinker, Yuval Alaluf, Amit H. Bermano, Daniel Cohen-Or, Ariel Shamir, Gal Chechik
A sketch is one of the most intuitive and versatile tools humans use to convey their ideas visually.
no code implementations • 6 Nov 2023 • Yuval Alaluf, Daniel Garibi, Or Patashnik, Hadar Averbuch-Elor, Daniel Cohen-Or
Recent advancements in text-to-image generative models have demonstrated a remarkable ability to capture a deep semantic understanding of images.
1 code implementation • 3 Aug 2023 • Elad Richardson, Kfir Goldberg, Yuval Alaluf, Daniel Cohen-Or
Recent text-to-image generative models have enabled us to transform our words into vibrant, captivating imagery.
1 code implementation • 24 May 2023 • Yuval Alaluf, Elad Richardson, Gal Metzer, Daniel Cohen-Or
We observe that one can significantly improve the convergence and visual fidelity of the concept by introducing a textual bypass, where our neural mapper additionally outputs a residual that is added to the output of the text encoder.
1 code implementation • 3 Feb 2023 • Elad Richardson, Gal Metzer, Yuval Alaluf, Raja Giryes, Daniel Cohen-Or
In this paper, we present TEXTure, a novel method for text-guided generation, editing, and transfer of textures for 3D shapes.
2 code implementations • 31 Jan 2023 • Hila Chefer, Yuval Alaluf, Yael Vinker, Lior Wolf, Daniel Cohen-Or
Recent text-to-image generative models have demonstrated an unparalleled ability to generate diverse and creative imagery guided by a target text prompt.
no code implementations • ICCV 2023 • Yael Vinker, Yuval Alaluf, Daniel Cohen-Or, Ariel Shamir
In this paper, we present a method for converting a given scene image into a sketch using different types and multiple levels of abstraction.
7 code implementations • 2 Aug 2022 • Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H. Bermano, Gal Chechik, Daniel Cohen-Or
Yet, it is unclear how such freedom can be exercised to generate images of specific unique concepts, modify their appearance, or compose them in new roles and novel scenes.
no code implementations • 28 Feb 2022 • Amit H. Bermano, Rinon Gal, Yuval Alaluf, Ron Mokady, Yotam Nitzan, Omer Tov, Or Patashnik, Daniel Cohen-Or
Of these, StyleGAN offers a fascinating case study, owing to its remarkable visual quality and an ability to support a large array of downstream tasks.
1 code implementation • 31 Jan 2022 • Yuval Alaluf, Or Patashnik, Zongze Wu, Asif Zamir, Eli Shechtman, Dani Lischinski, Daniel Cohen-Or
In particular, we demonstrate that while StyleGAN3 can be trained on unaligned data, one can still use aligned data for training, without hindering the ability to generate unaligned imagery.
1 code implementation • CVPR 2022 • Yuval Alaluf, Omer Tov, Ron Mokady, Rinon Gal, Amit H. Bermano
In this work, we introduce this approach into the realm of encoder-based inversion.
1 code implementation • 15 Jul 2021 • Omer Kafri, Or Patashnik, Yuval Alaluf, Daniel Cohen-Or
Inserting the resulting style code into a pre-trained StyleGAN generator results in a single harmonized image in which each semantic region is controlled by one of the input latent codes.
2 code implementations • ICCV 2021 • Yuval Alaluf, Or Patashnik, Daniel Cohen-Or
Instead of directly predicting the latent code of a given real image using a single pass, the encoder is tasked with predicting a residual with respect to the current estimate of the inverted latent code in a self-correcting manner.
2 code implementations • 4 Feb 2021 • Yuval Alaluf, Or Patashnik, Daniel Cohen-Or
In this formulation, our method approaches the continuous aging process as a regression task between the input age and desired target age, providing fine-grained control over the generated image.
8 code implementations • 4 Feb 2021 • Omer Tov, Yuval Alaluf, Yotam Nitzan, Or Patashnik, Daniel Cohen-Or
We then suggest two principles for designing encoders in a manner that allows one to control the proximity of the inversions to regions that StyleGAN was originally trained on.
10 code implementations • CVPR 2021 • Elad Richardson, Yuval Alaluf, Or Patashnik, Yotam Nitzan, Yaniv Azar, Stav Shapiro, Daniel Cohen-Or
We present a generic image-to-image translation framework, pixel2style2pixel (pSp).