Search Results for author:
Found 16 papers, 11 papers with code
Lumiere: A Space-Time Diffusion Model for Video Generation
We introduce Lumiere -- a text-to-video diffusion model designed for synthesizing videos that portray realistic, diverse and coherent motion -- a pivotal challenge in video synthesis.
Ranked #6 on
Text-to-Video Generation
on UCF-101
Idempotent Generative Network
We define the target manifold as the set of all instances that $f$ maps to themselves.
The Hidden Language of Diffusion Models
In this work, we present Conceptor, a novel method to interpret the internal representation of a textual concept by a diffusion model.
Teaching CLIP to Count to Ten
Our counting loss is deployed over automatically-created counterfactual examples, each consisting of an image and a caption containing an incorrect object count.
Imagic: Text-Based Real Image Editing with Diffusion Models
In this paper we demonstrate, for the very first time, the ability to apply complex (e. g., non-rigid) text-guided semantic edits to a single real image.
MyStyle: A Personalized Generative Prior
Given a small reference set of portrait images of a person (~100), we tune the weights of a pretrained StyleGAN face generator to form a local, low-dimensional, personalized manifold in the latent space.
Self-Distilled StyleGAN: Towards Generation from Internet Photos
To meet these challenges, we proposed a StyleGAN-based self-distillation approach, which consists of two main components: (i) A generative-based self-filtering of the dataset to eliminate outlier images, in order to generate an adequate training set, and (ii) Perceptual clustering of the generated images to detect the inherent data modalities, which are then employed to improve StyleGAN's "truncation trick" in the image synthesis process.
Deep Saliency Prior for Reducing Visual Distraction
Using only a model that was trained to predict where people look at images, and no additional training data, we can produce a range of powerful editing effects for reducing distraction in images.
Explaining in Style: Training a GAN to explain a classifier in StyleSpace
A natural source for such attributes is the StyleSpace of StyleGAN, which is known to generate semantically meaningful dimensions in the image.
SpeedNet: Learning the Speediness in Videos
We demonstrate how those learned features can boost the performance of self-supervised action recognition, and can be used for video retrieval.
Semantic Pyramid for Image Generation
We demonstrate that our model results in a versatile and flexible framework that can be used in various classic and novel image generation tasks.
Speech2Face: Learning the Face Behind a Voice
How much can we infer about a person's looks from the way they speak?
Looking to Listen at the Cocktail Party: A Speaker-Independent Audio-Visual Model for Speech Separation
Solving this task using only audio as input is extremely challenging and does not provide an association of the separated speech signals with speakers in the video.
XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
Style transfer usually refers to the task of applying color and texture information from a specific style image to a given content image while preserving the structure of the latter.
Synthesizing Normalized Faces from Facial Identity Features
We present a method for synthesizing a frontal, neutral-expression image of a person's face given an input face photograph.
Separating Signal from Noise Using Patch Recurrence across Scales
While clean patches are obscured by severe noise in the original scale of a noisy image, noise levels drop dramatically at coarser image scales.
