Image Manipulation

107 papers with code • 2 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.

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.

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.

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.

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.

Kornia: an Open Source Differentiable Computer Vision Library for PyTorch

kornia/kornia 5 Oct 2019

This work presents Kornia -- an open source computer vision library which consists of a set of differentiable routines and modules to solve generic computer vision problems.

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.

Point-to-Point Video Generation

charlescheng0117/p2pvg ICCV 2019

We introduce point-to-point video generation that controls the generation process with two control points: the targeted start- and end-frames.