Papers with Code 아카이브
Image Manipulation
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Image Manipulation is the process of altering or transforming an existing image to achieve a desired effect or to modify its content. This can involve various techniques and tools to enhance, modify, or create images based on specific requirements.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/rosinality/stylegan2-pytorch
https://github.com/rosinality/swapping-autoencoder-pytorch
https://github.com/ryyAudrey/ConMixSinGAN-master
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/septmars/DL
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/seungho-snu/fxsr
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
https://github.com/sony/nnabla-examples/tree/master/GANs/stylegan2-distillation
https://github.com/stelabou/stylegan_directions_face_reenactment
https://github.com/steveazzolin/SinGan3D
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/t-ae/singan-s4tf
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/taesungp/swapping-autoencoder-pytorch
https://github.com/takuseno/singan-nnabla
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/teddykoker/image-forensics
https://github.com/thevoidname/generalized-one-shot-gan-adaption
https://github.com/tohinz/ConSinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/tony23545/SinGAN_
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/tsujuifu/pytorch_mgie
Guiding Instruction-based Image Editing via Multimodal Large Language Models
https://github.com/ultranity/Paddle-StyleCLIP
https://github.com/valeoai/maskgit-pytorch
https://github.com/vcaptainv/SinGan-Data-Augumentation
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/vcaptainv/SinGan-video_texture
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/venkycode/re-singan
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/victorruelle/SinGAN-extension
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/vtddggg/BilinearGAN_for_LBIE
https://github.com/weihaox/Multi-Modal-CelebA-HQ-Dataset
Conditional Image Generation and Manipulation for User-Specified Content
https://github.com/woctezuma/steam-stylegan2
https://github.com/woozzu/dong_iccv_2017
https://github.com/wtjiang98/ManiGAN-baseline
https://github.com/xcyan/neurips18_hierchical_image_manipulation
Learning Hierarchical Semantic Image Manipulation through Structured Representations
https://github.com/xiaohangzhan/deocclusion
https://github.com/yangli-lab/artifact-eraser
https://github.com/yangyucheng000/SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/yccyenchicheng/p2pvg
https://github.com/yuval-alaluf/restyle-encoder
https://github.com/yuzq97/starter_project
Interpreting the Latent Space of GANs for Semantic Face Editing
https://github.com/zhangqianhui/Swapping-Autoencoder-tf
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.