https://github.com/aymenmir1/pix2surf
공식 코드Learning to Transfer Texture from Clothing Images to 3D Humans
Papers with Code 아카이브
Image-to-Image Translation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 11
- 평가표
- 38
- 보존된 평가 기록
- 195
Image-to-Image Translation is a task in computer vision and machine learning where the goal is to learn a mapping between an input image and an output image, such that the output image can be used to perform a specific task, such as style transfer, data augmentation, or image restoration.
<span style="color:grey; opacity: 0.6">( Image credit: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/azadis/SB-GAN
공식 코드https://github.com/babahui/fine-grained-appearance-transfer
공식 코드https://github.com/baidu/AdvBox
공식 코드A Tour of Convolutional Networks Guided by Linear Interpreters
https://github.com/batmanlab/mspc
공식 코드Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation
https://github.com/bayer-group/freggan
공식 코드fRegGAN with K-space Loss Regularization for Medical Image Translation
https://github.com/bcmi/object-shadow-generation-dataset-desobav2
공식 코드https://github.com/betterze/StyleAlign
공식 코드StyleAlign: Analysis and Applications of Aligned StyleGAN Models
https://github.com/binhuixie/spcl
공식 코드https://github.com/binuxliu/nocplace
공식 코드NocPlace: Nocturnal Visual Place Recognition via Generative and Inherited Knowledge Transfer
https://github.com/bit-da/sepico
공식 코드SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation
https://github.com/bluejangbaljang/shunit
공식 코드SHUNIT: Style Harmonization for Unpaired Image-to-Image Translation
https://github.com/blutjens/eie-earth-public
공식 코드Generating Physically-Consistent Satellite Imagery for Climate Visualizations
https://github.com/bonifazstuhr/feamgan
공식 코드Masked Discriminators for Content-Consistent Unpaired Image-to-Image Translation
https://github.com/boschresearch/OASIS
공식 코드You Only Need Adversarial Supervision for Semantic Image Synthesis
https://github.com/brownvc/ganimorph
공식 코드Improving Shape Deformation in Unsupervised Image-to-Image Translation
https://github.com/bupt-ai-cz/BCI
공식 코드BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix
https://github.com/bupt-ai-cz/IAST-ECCV2020
공식 코드Instance Adaptive Self-Training for Unsupervised Domain Adaptation
https://github.com/bupt-ai-cz/LLVIP
공식 코드LLVIP: A Visible-infrared Paired Dataset for Low-light Vision
https://github.com/bupt-ai-cz/hiast
공식 코드Hard-aware Instance Adaptive Self-training for Unsupervised Cross-domain Semantic Segmentation
https://github.com/bw-park/DBFS
공식 코드Stochastic Optimal Control for Diffusion Bridges in Function Spaces
https://github.com/bytedance/MoMA
공식 코드MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation
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/catlab-team/graph2pix
공식 코드Graph2Pix: A Graph-Based Image to Image Translation Framework
https://github.com/chen742/DCF
공식 코드Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation
https://github.com/chen742/PiPa
공식 코드PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation
https://github.com/chenwu98/cycle-diffusion
공식 코드Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance
https://github.com/chenwu98/unified-generative-zoo
공식 코드Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance
https://github.com/cics-nd/dcedn-gcs
공식 코드https://github.com/clear-nus/fdrl
공식 코드https://github.com/clovaai/stargan-v2
공식 코드https://github.com/clovaai/tunit
공식 코드Rethinking the Truly Unsupervised Image-to-Image Translation
https://github.com/costapt/vess2ret
공식 코드https://github.com/crystalneuro/visual-concept-translator
공식 코드General Image-to-Image Translation with One-Shot Image Guidance
https://github.com/csccm-iitd/glunet
공식 코드Gated Linear Model induced U-net for surrogate modeling and uncertainty quantification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.