Image Generation
1996 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 )
Benchmarks
These leaderboards are used to track progress in Image Generation
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Libraries
Use these libraries to find Image Generation models and implementationsDatasets
CIFAR-10
ImageNet
CIFAR-100
MNIST
Cityscapes
CelebA
Fashion-MNIST
CUB-200-2011
FFHQ
Oxford 102 Flower
Subtasks
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Image-to-Image Translation
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Image Inpainting
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Text-to-Image Generation
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Conditional Image Generation
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Conditional Image Generation
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Face Generation
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3D Generation
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Image Harmonization
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Pose Transfer
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3D-Aware Image Synthesis
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Facial Inpainting
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Layout-to-Image Generation
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ROI-based image generation
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Image Generation from Scene Graphs
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Pose-Guided Image Generation
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User Constrained Thumbnail Generation
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Handwritten Word Generation
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Chinese Landscape Painting Generation
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person reposing
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Infinite Image Generation
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Multi class one-shot image synthesis
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Single class few-shot image synthesis
Latest papers with no code
Synthesizing Iris Images using Generative Adversarial Networks: Survey and Comparative Analysis
In this paper, we present a comprehensive review of state-of-the-art GAN-based synthetic iris image generation techniques, evaluating their strengths and limitations in producing realistic and useful iris images that can be used for both training and testing iris recognition systems and presentation attack detectors.
BlenderAlchemy: Editing 3D Graphics with Vision-Language Models
Specifically, we design a vision-based edit generator and state evaluator to work together to find the correct sequence of actions to achieve the goal.
MuseumMaker: Continual Style Customization without Catastrophic Forgetting
To deal with catastrophic forgetting amongst past learned styles, we devise a dual regularization for shared-LoRA module to optimize the direction of model update, which could regularize the diffusion model from both weight and feature aspects, respectively.
Conditional Distribution Modelling for Few-Shot Image Synthesis with Diffusion Models
Few-shot image synthesis entails generating diverse and realistic images of novel categories using only a few example images.
Sketch2Human: Deep Human Generation with Disentangled Geometry and Appearance Control
This work presents Sketch2Human, the first system for controllable full-body human image generation guided by a semantic sketch (for geometry control) and a reference image (for appearance control).
SkinGEN: an Explainable Dermatology Diagnosis-to-Generation Framework with Interactive Vision-Language Models
With the continuous advancement of vision language models (VLMs) technology, remarkable research achievements have emerged in the dermatology field, the fourth most prevalent human disease category.
From Parts to Whole: A Unified Reference Framework for Controllable Human Image Generation
Addressing this, we introduce Parts2Whole, a novel framework designed for generating customized portraits from multiple reference images, including pose images and various aspects of human appearance.
Multimodal Large Language Model is a Human-Aligned Annotator for Text-to-Image Generation
Recent studies have demonstrated the exceptional potentials of leveraging human preference datasets to refine text-to-image generative models, enhancing the alignment between generated images and textual prompts.
FINEMATCH: Aspect-based Fine-grained Image and Text Mismatch Detection and Correction
To address this, we propose FineMatch, a new aspect-based fine-grained text and image matching benchmark, focusing on text and image mismatch detection and correction.
ID-Aligner: Enhancing Identity-Preserving Text-to-Image Generation with Reward Feedback Learning
The rapid development of diffusion models has triggered diverse applications.
