Computer Vision
 
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Image Generation

1979 papers with code • 85 benchmarks • 67 datasets

Image Generation (synthesis) is the task of generating new images from an existing dataset.

  • Unconditional generation refers to generating samples unconditionally from the dataset, i.e. $p(y)$
  • Conditional image generation (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.

In this section, you can find state-of-the-art leaderboards for unconditional generation. For conditional generation, and other types of image generations, refer to the subtasks.

( Image credit: StyleGAN )

Libraries

Use these libraries to find Image Generation models and implementations

Datasets

Subtasks

Latest papers with no code

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MoA: Mixture-of-Attention for Subject-Context Disentanglement in Personalized Image Generation

no code yet • 17 Apr 2024

MoA is designed to retain the original model's prior by fixing its attention layers in the prior branch, while minimally intervening in the generation process with the personalized branch that learns to embed subjects in the layout and context generated by the prior branch.

 
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Image Generative Semantic Communication with Multi-Modal Similarity Estimation for Resource-Limited Networks

no code yet • 17 Apr 2024

This method transmits only the semantic information of an image, and the receiver reconstructs the image using an image-generation model.

 
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Optical Image-to-Image Translation Using Denoising Diffusion Models: Heterogeneous Change Detection as a Use Case

no code yet • 17 Apr 2024

We introduce an innovative deep learning-based method that uses a denoising diffusion-based model to translate low-resolution images to high-resolution ones from different optical sensors while preserving the contents and avoiding undesired artifacts.

 
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SSDiff: Spatial-spectral Integrated Diffusion Model for Remote Sensing Pansharpening

no code yet • 17 Apr 2024

Pansharpening is a significant image fusion technique that merges the spatial content and spectral characteristics of remote sensing images to generate high-resolution multispectral images.

 
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On the Scalability of GNNs for Molecular Graphs

no code yet • 17 Apr 2024

However, structure-based architectures such as Graph Neural Networks (GNNs) are yet to show the benefits of scale mainly due to the lower efficiency of sparse operations, large data requirements, and lack of clarity about the effectiveness of various architectures.

 
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Generating Counterfactual Trajectories with Latent Diffusion Models for Concept Discovery

no code yet • 16 Apr 2024

In the first step, CDCT uses a Latent Diffusion Model (LDM) to generate a counterfactual trajectory dataset.

 
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OneActor: Consistent Character Generation via Cluster-Conditioned Guidance

no code yet • 16 Apr 2024

Comprehensive experiments show that our method outperforms a variety of baselines with satisfactory character consistency, superior prompt conformity as well as high image quality.

 
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Adversarial Identity Injection for Semantic Face Image Synthesis

no code yet • 16 Apr 2024

Among all the explored techniques, Semantic Image Synthesis (SIS) methods, whose goal is to generate an image conditioned on a semantic segmentation mask, are the most promising, even though preserving the perceived identity of the input subject is not their main concern.

 
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OmniSSR: Zero-shot Omnidirectional Image Super-Resolution using Stable Diffusion Model

no code yet • 16 Apr 2024

Omnidirectional images (ODIs) are commonly used in real-world visual tasks, and high-resolution ODIs help improve the performance of related visual tasks.

 
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Watermark-embedded Adversarial Examples for Copyright Protection against Diffusion Models

no code yet • 15 Apr 2024

Diffusion Models (DMs) have shown remarkable capabilities in various image-generation tasks.

 
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