SOTAVerified

Image Generation

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 )

Papers

Showing 110 of 6689 papers

TitleStatusHype
FashionPose: Text to Pose to Relight Image Generation for Personalized Fashion Visualization0
A Distributed Generative AI Approach for Heterogeneous Multi-Domain Environments under Data Sharing constraintsCode0
Pixel Perfect MegaMed: A Megapixel-Scale Vision-Language Foundation Model for Generating High Resolution Medical Images0
Synthesizing Reality: Leveraging the Generative AI-Powered Platform Midjourney for Construction Worker Detection0
fastWDM3D: Fast and Accurate 3D Healthy Tissue InpaintingCode0
FADE: Adversarial Concept Erasure in Flow Models0
CharaConsist: Fine-Grained Consistent Character GenerationCode2
Latent Space Consistency for Sparse-View CT Reconstruction0
CATVis: Context-Aware Thought Visualization0
Implementing Adaptations for Vision AutoRegressive ModelCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1VE (erel=0.01)FID26.46Unverified
2VE (erel=0.02)FID26.46Unverified
3BOSSclean-FID13.21Unverified
4TransGANFID8.94Unverified
5PNDMFID8.69Unverified
6Denoising Diffusion Probabilistic ModelFID7.89Unverified
7PGGANFID6.42Unverified
8LFMFID5.54Unverified
9DDGANFID5.25Unverified
10MSG-StyleGANFID5.2Unverified