https://github.com/c8241998/humanrig
공식 코드HumanRig: Learning Automatic Rigging for Humanoid Character in a Large Scale Dataset
Papers with Code 아카이브
Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/caiziruo/nf-ula
공식 코드NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problems
https://github.com/cameltr/rgtsi
공식 코드Reference-Guided Texture and Structure Inference for Image Inpainting
https://github.com/cameltr/transref
공식 코드TransRef: Multi-Scale Reference Embedding Transformer for Reference-Guided Image Inpainting
https://github.com/camilocarvajalreyes/sfws-stable-diffusion
공식 코드Towards SFW sampling for diffusion models via external conditioning
https://github.com/candlelabai/tpfnet
공식 코드TPFNet: A Novel Text In-painting Transformer for Text Removal
https://github.com/cantbebetter2/rdd
공식 코드Relational Diffusion Distillation for Efficient Image Generation
https://github.com/caojiezhang/MWGAN
공식 코드https://github.com/caradryanliang/improvedadvdm
공식 코드Targeted Attack Improves Protection against Unauthorized Diffusion Customization
https://github.com/caraj7/comat
공식 코드CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching
https://github.com/caraj7/t2i-r1
공식 코드T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoT
https://github.com/carolineec/EverybodyDanceNow
공식 코드https://github.com/carolineec/informative-drawings
공식 코드Learning to generate line drawings that convey geometry and semantics
https://github.com/castorini/daam
공식 코드What the DAAM: Interpreting Stable Diffusion Using Cross Attention
https://github.com/castorini/unirag
공식 코드UniRAG: Universal Retrieval Augmentation for Large Vision Language Models
https://github.com/casus/phirm
공식 코드Phenotype-preserving metric design for high-content image reconstruction by generative inpainting
https://github.com/casus/pi-ddpm
공식 코드Microscopy image reconstruction with physics-informed denoising diffusion probabilistic model
https://github.com/catlab-team/graph2pix
공식 코드Graph2Pix: A Graph-Based Image to Image Translation Framework
https://github.com/catlab-team/latentclr
공식 코드LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions
https://github.com/causalfusion/causalfusion
공식 코드https://github.com/cbhu523/saliency_last_way_finding
공식 코드A Saliency-Guided Street View Image Inpainting Framework for Efficient Last-Meters Wayfinding
https://github.com/cc-ai/climategan
공식 코드ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods
https://github.com/cc-ai/mila-simulated-floods
공식 코드ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods
https://github.com/cc-hpc-itwm/facialgan
공식 코드FacialGAN: Style Transfer and Attribute Manipulation on Synthetic Faces
https://github.com/ccccczzy/icc
공식 코드https://github.com/cccrrrccc/fp4dit
공식 코드FP4DiT: Towards Effective Floating Point Quantization for Diffusion Transformers
https://github.com/ccitachi/PSPStain
공식 코드Pathological Semantics-Preserving Learning for H&E-to-IHC Virtual Staining
https://github.com/celebv-hq/celebv-hq
공식 코드https://github.com/ch3ngy1/controllable-image-generation
공식 코드Robust Image Ordinal Regression with Controllable Image Generation
https://github.com/chang-yuanyuan/caso
공식 코드Instructing Text-to-Image Diffusion Models via Classifier-Guided Semantic Optimization
https://github.com/chang810249/BEGAN-CS
공식 코드https://github.com/changsn/FlexDiT
공식 코드FlexDiT: Dynamic Token Density Control for Diffusion Transformer
https://github.com/changyuanwang17/apq-dm
공식 코드Towards Accurate Post-training Quantization for Diffusion Models
https://github.com/chaofantao/autoregressive-models-in-vision-survey
공식 코드https://github.com/chaoyuesong/3d-corenet
공식 코드3D Pose Transfer with Correspondence Learning and Mesh Refinement
https://github.com/character-adapter/character-adapter
공식 코드Character-Adapter: Prompt-Guided Region Control for High-Fidelity Character Customization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.