Image Manipulation

140 papers with code • 1 benchmarks • 4 datasets

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Use these libraries to find Image Manipulation models and implementations

Most implemented papers

SinGAN: Learning a Generative Model from a Single Natural Image

tamarott/SinGAN ICCV 2019

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 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.

MaskGAN: Towards Diverse and Interactive Facial Image Manipulation

switchablenorms/CelebAMask-HQ CVPR 2020

To overcome these drawbacks, we propose a novel framework termed MaskGAN, enabling diverse and interactive face manipulation.

Controlling Perceptual Factors in Neural Style Transfer

leongatys/NeuralImageSynthesis CVPR 2017

Neural Style Transfer has shown very exciting results enabling new forms of image manipulation.

SRFlow: Learning the Super-Resolution Space with Normalizing Flow

andreas128/SRFlow ECCV 2020

SRFlow therefore directly accounts for the ill-posed nature of the problem, and learns to predict diverse photo-realistic high-resolution images.

Designing an Encoder for StyleGAN Image Manipulation

omertov/encoder4editing 4 Feb 2021

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.

StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery

orpatashnik/StyleCLIP 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.

MaskGIT: Masked Generative Image Transformer

google-research/maskgit 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.

Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold

XingangPan/DragGAN 18 May 2023

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

ShenYujun/InterFaceGAN CVPR 2020

In this work, we propose a novel framework, called InterFaceGAN, for semantic face editing by interpreting the latent semantics learned by GANs.