367 citations · 446 across the 17 of their papers we have counts for
17 papers
Token-to-Token Alignment of Text Embeddings for Semantic Blending
Saar Huberman, Ron Mokady, Or Patashnik +1
In modern generative models, images are specified and controlled through text prompts. In practice, images are generated from sequences of tokens derived from these prompts. Howeve…
BBQ-to-Image: Numeric Bounding Box and Qolor Control in Large-Scale Text-to-Image Models
Eliran Kachlon, Alexander Visheratin, Nimrod Sarid +6
Text-to-image models have rapidly advanced in realism and controllability, with recent approaches leveraging long, detailed captions to support fine-grained generation. However, a…
SemanticMoments: Training-Free Motion Similarity via Third Moment Features
Saar Huberman, Kfir Goldberg, Or Patashnik +2
Retrieving videos based on semantic motion is a fundamental, yet unsolved, problem. Existing video representation approaches overly rely on static appearance and scene context rath…
Generating an Image From 1,000 Words: Enhancing Text-to-Image With Structured Captions
Eyal Gutflaish, Eliran Kachlon, Hezi Zisman +8
Text-to-image models have rapidly evolved from casual creative tools to professional-grade systems, achieving unprecedented levels of image quality and realism. Yet, most models ar…
Image Generation from Contextually-Contradictory Prompts
Saar Huberman, Or Patashnik, Omer Dahary +2
Text-to-image diffusion models excel at generating high-quality, diverse images from natural language prompts. However, they often fail to produce semantically accurate results whe…
Null-text Inversion for Editing Real Images using Guided Diffusion Models
Ron Mokady, Amir Hertz, Kfir Aberman +2
Recent text-guided diffusion models provide powerful image generation capabilities. Currently, a massive effort is given to enable the modification of these images using text only…
Text-Only Training for Image Captioning using Noise-Injected CLIP
David Nukrai, Ron Mokady, Amir Globerson
We consider the task of image-captioning using only the CLIP model and additional text data at training time, and no additional captioned images. Our approach relies on the fact th…
Prompt-to-Prompt Image Editing with Cross Attention Control
Amir Hertz, Ron Mokady, Jay Tenenbaum +3
Recent large-scale text-driven synthesis models have attracted much attention thanks to their remarkable capabilities of generating highly diverse images that follow given text pro…
State-of-the-Art in the Architecture, Methods and Applications of StyleGAN
Amit H. Bermano, Rinon Gal, Yuval Alaluf +5
Generative Adversarial Networks (GANs) have established themselves as a prevalent approach to image synthesis. Of these, StyleGAN offers a fascinating case study, owing to its rema…
Self-Distilled StyleGAN: Towards Generation from Internet Photos
Ron Mokady, Michal Yarom, Omer Tov +5
StyleGAN is known to produce high-fidelity images, while also offering unprecedented semantic editing. However, these fascinating abilities have been demonstrated only on a limited…
Stitch it in Time: GAN-Based Facial Editing of Real Videos
Rotem Tzaban, Ron Mokady, Rinon Gal +2
The ability of Generative Adversarial Networks to encode rich semantics within their latent space has been widely adopted for facial image editing. However, replicating their succe…
ClipCap: CLIP Prefix for Image Captioning
Ron Mokady, Amir Hertz, Amit H. Bermano
Image captioning is a fundamental task in vision-language understanding, where the model predicts a textual informative caption to a given input image. In this paper, we present a…
HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing
Yuval Alaluf, Omer Tov, Ron Mokady +2
The inversion of real images into StyleGAN's latent space is a well-studied problem. Nevertheless, applying existing approaches to real-world scenarios remains an open challenge, d…
JOKR: Joint Keypoint Representation for Unsupervised Cross-Domain Motion Retargeting
Ron Mokady, Rotem Tzaban, Sagie Benaim +2
The task of unsupervised motion retargeting in videos has seen substantial advancements through the use of deep neural networks. While early works concentrated on specific object p…
Pivotal Tuning for Latent-based Editing of Real Images
Daniel Roich, Ron Mokady, Amit H. Bermano +1
Recently, a surge of advanced facial editing techniques have been proposed that leverage the generative power of a pre-trained StyleGAN. To successfully edit an image this way, one…
Structural-analogy from a Single Image Pair
Sagie Benaim, Ron Mokady, Amit Bermano +2
The task of unsupervised image-to-image translation has seen substantial advancements in recent years through the use of deep neural networks. Typically, the proposed solutions lea…
Mask Based Unsupervised Content Transfer
Ron Mokady, Sagie Benaim, Lior Wolf +1
We consider the problem of translating, in an unsupervised manner, between two domains where one contains some additional information compared to the other. The proposed method dis…