Papers with Code 아카이브
3D Face Reconstruction
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3D Face Reconstruction is a computer vision task that involves creating a 3D model of a human face from a 2D image or a set of images. The goal of 3D face reconstruction is to reconstruct a digital 3D representation of a person's face, which can be used for various applications such as animation, virtual reality, and biometric identification.
<span style="color:grey; opacity: 0.6">( Image credit: 3DDFA_V2 )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/pengrenlai/RetinaNet-tensorflow
https://github.com/pengsongyou/OMG-ADSC
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/penincillin/DREAM
Pose-Robust Face Recognition via Deep Residual Equivariant Mapping
https://github.com/peterhan91/thorax_gan
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/petermankowski510/facenet_test
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/peteryuX/arcface-tf2
https://github.com/peteryuX/esrgan-tf2
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/peteryuX/retinaface-tf2
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/petrum01/Capstone_project_object_detection
https://github.com/pgtinsley/insightface_oge
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/phamquiluan/ResidualMaskingNetwork
Challenges in Representation Learning: A report on three machine learning contests
https://github.com/phillity/BioCapsule
https://github.com/phiresky/pandoc-url2cite
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/phongdinhv/triplet-loss-keras-mnist
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/phuijse/MATIC
https://github.com/piaohe111/gittest
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/pierluigiferrari/ssd_keras
https://github.com/pikabite/segmentations_tf2
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/pikkaay/efficientnet_gpu
https://github.com/piotrkawa/audio-deepfake-source-tracing
https://github.com/pipidog/CNLP
https://github.com/playerkk/face-py-faster-rcnn
https://github.com/pocheck-v2/Pocheck-V2-Beta
https://github.com/podgorskiy/StyleGan
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/pohlinwei/AComPianist
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/pohwa065/Deep-Learning-for-Super-Resolution-and-Image-Enhancement
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/pohwa065/SRGAN-for-Super-Resolution-and-Image-Enhancement
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/polarisZhao/PFLD-pytorch
https://github.com/polarisZhao/mtcnn-pytorch
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/polimi-ispl/icpr2020dfdc
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/politecat314/MEBeauty_CNN
https://github.com/pothabattulasantosh/Detection-of-face-Manipulated-videos
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/pothabattulasantosh/Detection-of-facial-Manipulated-videos
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/pouya-parsa/progressive_gan
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/ppokhare/Handling-Imbalanced-Dataset-for-classification-task
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.