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/yangyucheng000/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/yangyucheng000/sphereface
https://github.com/yanqi1811/attention-is-all-you-need
https://github.com/yanx27/Pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/yanxp/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/yashraj02/RetinaFace
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/yaxingwang/MineGAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/ycxioooong/MovieSynopsisAssociation
Unifying Identification and Context Learning for Person Recognition
https://github.com/yehengchen/FaceNet-FaceRecognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/yehengchen/FaceRecognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/yehengchen/FaceRecognition-FaceNet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/yehengchen/SmartCar-FaceRecognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/yellowfighter2333/ticket-machine-with-face-recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/yellowfighter2333/ticket-mechine-with-face-recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/yerfor/geneface
GeneFace: Generalized and High-Fidelity Audio-Driven 3D Talking Face Synthesis
https://github.com/yevgm/deepadv3d
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/yfeng95/deca
VGGFace2: A dataset for recognising faces across pose and age
https://github.com/yfsin/deepfake_pro
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/yh-iro/Keras-TP-GAN
https://github.com/yhenon/pytorch-retinanet
https://github.com/yifeizhou02/bt-2
https://github.com/yijiaceline/Final-Project-Group4
https://github.com/yijiazh/DFER_Summer2019
https://github.com/yiningzeng/darknet-fork-from-AlexeyAB
https://github.com/yiyuan1991/3D-Face-GCNs
https://github.com/yoboget/CProGAN-ME
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/yoshitomo-matsubara/torchdistill
https://github.com/ypeleg/komplex
https://github.com/ytng001/sensemaking
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ytsheng/sketch2cat-pulse
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/yuanyuanli85/tf-hrnet
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/yubangji123/Interpret_FR
https://github.com/yudie433/darknet
https://github.com/yuemenglong/deep-learning-copy
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/yueyongjiao/Transformer-Contextual
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.