7 citations · 10 across the 7 of their papers we have counts for
9 papers
Reference-Driven Multi-Speaker Audio Scene Generation from In-the-Wild Priors
Michael Finkelson, Daniel Segal, Eitan Richardson +7
Existing multi-speaker dialogue systems bind speakers to utterances through structured supervision: per-turn tags, multi-stream transcriptions, or learnable speaker embeddings. The…
LTX-2: Efficient Joint Audio-Visual Foundation Model
Yoav HaCohen, Benny Brazowski, Nisan Chiprut +26
Recent text-to-video diffusion models can generate compelling video sequences, yet they remain silent -- missing the semantic, emotional, and atmospheric cues that audio provides.…
LTX-Video: Realtime Video Latent Diffusion
Yoav HaCohen, Nisan Chiprut, Benny Brazowski +13
We introduce LTX-Video, a transformer-based latent diffusion model that adopts a holistic approach to video generation by seamlessly integrating the responsibilities of the Video-V…
Diffusing Colors: Image Colorization with Text Guided Diffusion
Nir Zabari, Aharon Azulay, Alexey Gorkor +2
The colorization of grayscale images is a complex and subjective task with significant challenges. Despite recent progress in employing large-scale datasets with deep neural networ…
Semantic Segmentation In-the-Wild Without Seeing Any Segmentation Examples
Nir Zabari, Yedid Hoshen
Semantic segmentation is a key computer vision task that has been actively researched for decades. In recent years, supervised methods have reached unprecedented accuracy, however…
Image Shape Manipulation from a Single Augmented Training Sample
Yael Vinker, Eliahu Horwitz, Nir Zabari +1
In this paper, we present DeepSIM, a generative model for conditional image manipulation based on a single image. We find that extensive augmentation is key for enabling single ima…
Use and Perceptions of Multi-Monitor Workstations: A Natural Experiment
Guy Amir, Ayala Prusak, Tal Reiss +2
Using multiple monitors is commonly thought to improve productivity, but this is hard to check experimentally. We use a survey, taken by 101 practitioners of which 80% have coded p…
Image Shape Manipulation from a Single Augmented Training Sample
Yael Vinker, Eliahu Horwitz, Nir Zabari +1
In this paper, we present DeepSIM, a generative model for conditional image manipulation based on a single image. We find that extensive augmentation is key for enabling single ima…
Training End-to-end Single Image Generators without GANs
Yael Vinker, Nir Zabari, Yedid Hoshen
We present AugurOne, a novel approach for training single image generative models. Our approach trains an upscaling neural network using non-affine augmentations of the (single) in…