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Image Generation
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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.
<span style="color:grey; opacity: 0.6">( Image credit: StyleGAN )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Identifying Drift, Diffusion, and Causal Structure from Temporal Snapshots
VisualSphinx: Large-Scale Synthetic Vision Logic Puzzles for RL
PixelPonder: Dynamic Patch Adaptation for Enhanced Multi-Conditional Text-to-Image Generation
NoHumansRequired: Autonomous High-Quality Image Editing Triplet Mining
Pixel Perfect MegaMed: A Megapixel-Scale Vision-Language Foundation Model for Generating High Resolution Medical Images
A Distributed Generative AI Approach for Heterogeneous Multi-Domain Environments under Data Sharing constraints
Beyond Fully Supervised Pixel Annotations: Scribble-Driven Weakly-Supervised Framework for Image Manipulation Localization
fastWDM3D: Fast and Accurate 3D Healthy Tissue Inpainting
Synthesizing Reality: Leveraging the Generative AI-Powered Platform Midjourney for Construction Worker Detection
FashionPose: Text to Pose to Relight Image Generation for Personalized Fashion Visualization
AutoPartGen: Autogressive 3D Part Generation and Discovery
Non-Adaptive Adversarial Face Generation
FADE: Adversarial Concept Erasure in Flow Models
PhysX: Physical-Grounded 3D Asset Generation
Latent Space Consistency for Sparse-View CT Reconstruction
MFGDiffusion: Mask-Guided Smoke Synthesis for Enhanced Forest Fire Detection
Implementing Adaptations for Vision AutoRegressive Model
CATVis: Context-Aware Thought Visualization
CharaConsist: Fine-Grained Consistent Character Generation
MGVQ: Could VQ-VAE Beat VAE? A Generalizable Tokenizer with Multi-group Quantization
RePaintGS: Reference-Guided Gaussian Splatting for Realistic and View-Consistent 3D Scene Inpainting
Vision Foundation Models as Effective Visual Tokenizers for Autoregressive Image Generation
Evaluating Attribute Confusion in Fashion Text-to-Image Generation
Model-free Optical Processors using In Situ Reinforcement Learning with Proximal Policy Optimization
AdaptaGen: Domain-Specific Image Generation through Hierarchical Semantic Optimization Framework
DreamArt: Generating Interactable Articulated Objects from a Single Image
USIGAN: Unbalanced Self-Information Feature Transport for Weakly Paired Image IHC Virtual Staining
TextPixs: Glyph-Conditioned Diffusion with Character-Aware Attention and OCR-Guided Supervision
MEDTalk: Multimodal Controlled 3D Facial Animation with Dynamic Emotions by Disentangled Embedding
NeoBabel: A Multilingual Open Tower for Visual Generation
OmniPart: Part-Aware 3D Generation with Semantic Decoupling and Structural Cohesion
S$^2$Edit: Text-Guided Image Editing with Precise Semantic and Spatial Control
Trojan Horse Prompting: Jailbreaking Conversational Multimodal Models by Forging Assistant Message
DC-AR: Efficient Masked Autoregressive Image Generation with Deep Compression Hybrid Tokenizer
Estimating Object Physical Properties from RGB-D Vision and Depth Robot Sensors Using Deep Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.