Papers with Code 아카이브
Conditional Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
- 11
- 보존된 평가 기록
- 75
Conditional image generation is the task of generating new images from a dataset conditional on their class.
<span style="color:grey; opacity: 0.6">( Image credit: PixelCNN++ )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/google-research/masksketch
공식 코드MaskSketch: Unpaired Structure-guided Masked Image Generation
https://github.com/google/compare_gan
공식 코드https://github.com/google/tim-gan
공식 코드Text as Neural Operator: Image Manipulation by Text Instruction
https://github.com/grigorisg9gr/polynomial_nets
공식 코드https://github.com/grigorisg9gr/polynomial_nets_for_conditional_generation
공식 코드CoPE: Conditional image generation using Polynomial Expansions
https://github.com/grigorisg9gr/rocgan
공식 코드https://github.com/gulnazaki/counterfactual-benchmark
공식 코드https://github.com/hakansivuk/divsem
공식 코드https://github.com/haoosz/BiGR
공식 코드https://github.com/hasibzunair/adversarial-lesions
공식 코드Melanoma Detection using Adversarial Training and Deep Transfer Learning
https://github.com/hecoding/hyper-modulation
공식 코드Transferring Unconditional to Conditional GANs with Hyper-Modulation
https://github.com/hhu-mmbs/cedm-official-wavc2025
공식 코드Rethinking cluster-conditioned diffusion models for label-free image synthesis
https://github.com/hitsz-tmg/agentic-cigeval
공식 코드A Unified Agentic Framework for Evaluating Conditional Image Generation
https://github.com/houliangict/adcgan
공식 코드https://github.com/hp-l33/ARPG
공식 코드Autoregressive Image Generation with Randomized Parallel Decoding
https://github.com/igul222/improved_wgan_training
공식 코드https://github.com/ilyakava/BigGAN-PyTorch
공식 코드https://github.com/ilyakava/gan
공식 코드https://github.com/infiniq-ai1/clipvqdiffusion
공식 코드https://github.com/j1anglin/ReCorD
공식 코드ReCorD: Reasoning and Correcting Diffusion for HOI Generation
https://github.com/jackyjsy/scgan
공식 코드https://github.com/jchemseddine/conditional_wasserstein_distances
공식 코드Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching
https://github.com/jiuntian/interactdiffusion
공식 코드InteractDiffusion: Interaction Control in Text-to-Image Diffusion Models
https://github.com/joyhuyy1412/personahoi
공식 코드PersonaHOI: Effortlessly Improving Personalized Face with Human-Object Interaction Generation
https://github.com/jwyang/lr-gan.pytorch
공식 코드LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation
https://github.com/kakaobrain/rq-vae-transformer
공식 코드https://github.com/kennymckormick/Triplet-Representation-of-human-Body
공식 코드TRB: A Novel Triplet Representation for Understanding 2D Human Body
https://github.com/kkanshul/finegan
공식 코드FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery
https://github.com/klory/MPG_Arxiv
공식 코드MPG: A Multi-ingredient Pizza Image Generator with Conditional StyleGANs
https://github.com/klory/mpg2
공식 코드Multi-attribute Pizza Generator: Cross-domain Attribute Control with Conditional StyleGAN
https://github.com/kshitij79/CS-7476-Improvements-in-Diffusion-Model
공식 코드Enhancing Conditional Image Generation with Explainable Latent Space Manipulation
https://github.com/lengsicong/tell2design
공식 코드Tell2Design: A Dataset for Language-Guided Floor Plan Generation
https://github.com/leolyliu/core4d-instructions
공식 코드CORE4D: A 4D Human-Object-Human Interaction Dataset for Collaborative Object REarrangement
https://github.com/liaopeiyuan/artbench
공식 코드The ArtBench Dataset: Benchmarking Generative Models with Artworks
https://github.com/lucidrains/imagen-pytorch
공식 코드Tell2Design: A Dataset for Language-Guided Floor Plan Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.