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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.
SV-DRR: High-Fidelity Novel View X-Ray Synthesis Using Diffusion Model
CorrDetail: Visual Detail Enhanced Self-Correction for Face Forgery Detection
LoomNet: Enhancing Multi-View Image Generation via Latent Space Weaving
Acquiring and Adapting Priors for Novel Tasks via Neural Meta-Architectures
DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge
A Training-Free Style-Personalization via Scale-wise Autoregressive Model
Flow-Anchored Consistency Models
Hita: Holistic Tokenizer for Autoregressive Image Generation
UniMC: Taming Diffusion Transformer for Unified Keypoint-Guided Multi-Class Image Generation
Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis
Locality-aware Parallel Decoding for Efficient Autoregressive Image Generation
Process-aware and high-fidelity microstructure generation using stable diffusion
UniGlyph: Unified Segmentation-Conditioned Diffusion for Precise Visual Text Synthesis
MTADiffusion: Mask Text Alignment Diffusion Model for Object Inpainting
Ovis-U1 Technical Report
CycleVAR: Repurposing Autoregressive Model for Unsupervised One-Step Image Translation
Prompt Mechanisms in Medical Imaging: A Comprehensive Survey
PhysRig: Differentiable Physics-Based Skinning and Rigging Framework for Realistic Articulated Object Modeling
ThermalDiffusion: Visual-to-Thermal Image-to-Image Translation for Autonomous Navigation
Rethink Sparse Signals for Pose-guided Text-to-image Generation
HybridQ: Hybrid Classical-Quantum Generative Adversarial Network for Skin Disease Image Generation
Instella-T2I: Pushing the Limits of 1D Discrete Latent Space Image Generation
Improving Diffusion-Based Image Editing Faithfulness via Guidance and Scheduling
Geometry and Perception Guided Gaussians for Multiview-consistent 3D Generation from a Single Image
BitMark for Infinity: Watermarking Bitwise Autoregressive Image Generative Models
GenFlow: Interactive Modular System for Image Generation
XVerse: Consistent Multi-Subject Control of Identity and Semantic Attributes via DiT Modulation
$\textrm{ODE}_t \left(\textrm{ODE}_l \right)$: Shortcutting the Time and Length in Diffusion and Flow Models for Faster Sampling
EAR: Erasing Concepts from Unified Autoregressive Models
Med-Art: Diffusion Transformer for 2D Medical Text-to-Image Generation
HiWave: Training-Free High-Resolution Image Generation via Wavelet-Based Diffusion Sampling
WonderFree: Enhancing Novel View Quality and Cross-View Consistency for 3D Scene Exploration
Video Perception Models for 3D Scene Synthesis
Diffusion Tree Sampling: Scalable inference-time alignment of diffusion models
3DGH: 3D Head Generation with Composable Hair and Face
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.