Image Manipulation
157 papers with code • 1 benchmarks • 4 datasets
Libraries
Use these libraries to find Image Manipulation models and implementations
CelebAMask-HQ
LRS2
CASIA V2
Satire Dataset
Most implemented papers
SinGAN: Learning a Generative Model from a Single Natural Image
We introduce SinGAN, an unconditional generative model that can be learned from a single natural image.
Closed-Form Factorization of Latent Semantics in GANs
rosinality/stylegan2-pytorch
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CVPR 2021
A rich set of interpretable dimensions has been shown to emerge in the latent space of the Generative Adversarial Networks (GANs) trained for synthesizing images.
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.
MaskGAN: Towards Diverse and Interactive Facial Image Manipulation
switchablenorms/CelebAMask-HQ
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CVPR 2020
To overcome these drawbacks, we propose a novel framework termed MaskGAN, enabling diverse and interactive face manipulation.
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
andreas128/SRFlow
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ECCV 2020
SRFlow therefore directly accounts for the ill-posed nature of the problem, and learns to predict diverse photo-realistic high-resolution images.
Controlling Perceptual Factors in Neural Style Transfer
leongatys/NeuralImageSynthesis
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CVPR 2017
Neural Style Transfer has shown very exciting results enabling new forms of image manipulation.
MaskGIT: Masked Generative Image Transformer
google-research/maskgit
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CVPR 2022
At inference time, the model begins with generating all tokens of an image simultaneously, and then refines the image iteratively conditioned on the previous generation.
StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery
orpatashnik/StyleCLIP
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ICCV 2021
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.
Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold
Synthesizing visual content that meets users' needs often requires flexible and precise controllability of the pose, shape, expression, and layout of the generated objects.
Interpreting the Latent Space of GANs for Semantic Face Editing
genforce/interfacegan
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CVPR 2020
In this work, we propose a novel framework, called InterFaceGAN, for semantic face editing by interpreting the latent semantics learned by GANs.
