https://github.com/LonglongaaaGo/EXE-GAN
공식 코드Do Inpainting Yourself: Generative Facial Inpainting Guided by Exemplars
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Facial inpainting (or face completion) is the task of generating plausible facial structures for missing pixels in a face image.
<span style="color:grey; opacity: 0.6">( Image credit: SymmFCNet )</span>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Do Inpainting Yourself: Generative Facial Inpainting Guided by Exemplars
E4S: Fine-grained Face Swapping via Editing With Regional GAN Inversion
E4S: Fine-grained Face Swapping via Editing With Regional GAN Inversion
Compensation Sampling for Improved Convergence in Diffusion Models
PATMAT: Person Aware Tuning of Mask-Aware Transformer for Face Inpainting
FCSR-GAN: Joint Face Completion and Super-resolution via Multi-task Learning
Reference-Guided Large-Scale Face Inpainting with Identity and Texture Control
SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
Detecting Overfitting of Deep Generative Networks via Latent Recovery
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.