Search Results for author:
Found 17 papers, 11 papers with code
CLiC: Concept Learning in Context
This paper addresses the challenge of learning a local visual pattern of an object from one image, and generating images depicting objects with that pattern.
Cross-Image Attention for Zero-Shot Appearance Transfer
Recent advancements in text-to-image generative models have demonstrated a remarkable ability to capture a deep semantic understanding of images.
Noise-Free Score Distillation
Score Distillation Sampling (SDS) has emerged as the de facto approach for text-to-content generation in non-image domains.
Localizing Object-level Shape Variations with Text-to-Image Diffusion Models
In this paper, we present a technique to generate a collection of images that depicts variations in the shape of a specific object, enabling an object-level shape exploration process.
Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures
This unique combination of text and shape guidance allows for increased control over the generation process.
Ranked #3 on
Text to 3D
on T$^3$Bench
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
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.
State-of-the-Art in the Architecture, Methods and Applications of StyleGAN
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.
FEAT: Face Editing with Attention
In this paper, we build on the StyleGAN generator, and present a method that explicitly encourages face manipulation to focus on the intended regions by incorporating learned attention maps.
Third Time's the Charm? Image and Video Editing with StyleGAN3
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.
StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators
Can a generative model be trained to produce images from a specific domain, guided by a text prompt only, without seeing any image?
StyleFusion: A Generative Model for Disentangling Spatial Segments
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.
ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement
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.
StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery
Inspired by the ability of StyleGAN to generate highly realistic images in a variety of domains, much recent work has focused on understanding how to use the latent spaces of StyleGAN to manipulate generated and real images.
Only a Matter of Style: Age Transformation Using a Style-Based Regression Model
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.
Designing an Encoder for StyleGAN Image Manipulation
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.
BalaGAN: Image Translation Between Imbalanced Domains via Cross-Modal Transfer
State-of-the-art image-to-image translation methods tend to struggle in an imbalanced domain setting, where one image domain lacks richness and diversity.
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
We present a generic image-to-image translation framework, pixel2style2pixel (pSp).
