computer-vision-10

Image Manipulation

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in image-manipulation-10

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LRS2

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Use these libraries to find image-manipulation-10 models and implementations

orpatashnik/StyleCLIP

2 papers 3,889

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Most implemented papers

SinGAN: Learning a Generative Model From a Single Natural Image

Tali Dekel, T. Michaeli, Tamar Rott Shaham•Wed May 01 2019

SinGAN, an unconditional generative model that can be learned from a single natural image, is introduced, trained to capture the internal distribution of patches within the image, and is then able to generate high quality, diverse samples that carry the same visual content as the image.

913

Content

PruneTruong/DenseMatching

2 papers 614

pkorus/neural-imaging

2 papers 154

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Closed-Form Factorization of Latent Semantics in GANs

Bolei Zhou, Yujun Shen•Sun Jul 12 2020

This work examines the internal representation learned by GANs to reveal the underlying variation factors in an unsupervised manner and proposes a closedform factorization algorithm for latent semantic discovery by directly decomposing the pre-trained weights.

633 0

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Designing an encoder for StyleGAN image manipulation

Omer Tov, Yuval Alaluf, Yotam Nitzan, Or Patashnik, D. Cohen-Or•Wed Feb 03 2021

This paper carefully study the latent space of StyleGAN, the state-of-the-art unconditional generator, and suggests 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.

868 0

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MaskGAN: Towards Diverse and Interactive Facial Image Manipulation

Ziwei Liu, Ping Luo, Cheng-Han Lee, Lingyun Wu•Fri Jul 26 2019

This work proposes a novel framework termed MaskGAN, enabling diverse and interactive face manipulation, and finds that semantic masks serve as a suitable intermediate representation for flexible face manipulation with fidelity preservation.

1221 0

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SRFlow: Learning the Super-Resolution Space with Normalizing Flow

L. Gool, Martin Danelljan, Radu Timofte, Andreas Lugmayr•Wed Jun 24 2020

The proposed SRFlow is a normalizing flow based super-resolution method capable of learning the conditional distribution of the output given the low-resolution input, and directly accounts for the ill-posed nature of the problem, and learns to predict diverse photo-realistic high-resolution images.

406 0

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Controlling Perceptual Factors in Neural Style Transfer

M. Bethge, Eli Shechtman, Aaron Hertzmann, Alexander S. Ecker, Leon A. Gatys•Tue Nov 22 2016

The existing Neural Style Transfer method is extended to introduce control over spatial location, colour information and across spatial scale, enabling the combination of style information from multiple sources to generate new, perceptually appealing styles from existing ones.

490 0

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MaskGIT: Masked Generative Image Transformer

Lu Jiang, Ce Liu, Huiwen Chang, Han Zhang, W. Freeman•Mon Feb 07 2022

The proposed MaskGIT is a novel image synthesis paradigm using a bidirectional transformer decoder that significantly outperforms the state-of-the-art transformer model on the ImageNet dataset, and accelerates autoregressive decoding by up to 48x.

922 0

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StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery

Eli Shechtman, D. Lischinski, Or Patashnik, D. Cohen-Or, Zongze Wu•Tue Mar 30 2021

This work explores leveraging the power of recently introduced Contrastive Language-Image Pre-training (CLIP) models in order to develop a text-based interface for StyleGAN image manipulation that does not require such manual effort.

1383 0

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

A. Tewari, C. Theobalt, Lingjie Liu, Xingang Pan, Thomas Leimkühler, Abhimitra Meka•Wed May 17 2023

This work proposes DragGAN, a powerful yet much less explored way of controlling GANs to "drag" any points of the image to precisely reach target points in a user-interactive manner, which consists of a feature-based motion supervision that drives the handle point to move towards the target position, and a new point tracking approach that leverages the discriminative generator features to keep localizing the position of the handle points.

322 0

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Interpreting the Latent Space of GANs for Semantic Face Editing

Xiaoou Tang, Bolei Zhou, Jinjin Gu, Yujun Shen•Wed Jul 24 2019

This work proposes a novel framework, called InterFaceGAN, for semantic face editing by interpreting the latent semantics learned by GANs, and finds that the latent code of well-trained generative models actually learns a disentangled representation after linear transformations.

1216 0

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