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/2023-MindSpore-4/Code-5/tree/main/MaskedFaceRecognition
https://github.com/2023-MindSpore-4/Code-5/tree/main/lresnet100e_ir
https://github.com/2023-MindSpore-4/Code1/tree/main/Arcface
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/2023-MindSpore-4/Code10/tree/main/Arcface
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/2023-MindSpore-4/Code10/tree/main/ResNet
https://github.com/2023-MindSpore-4/Code10/tree/main/RetinaFace_ResNet50
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/2023-MindSpore-4/Code10/tree/main/RetinaNet
https://github.com/2023-MindSpore-4/Code10/tree/main/VGG
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/2023-MindSpore-4/Code10/tree/main/frustum-pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/2023-MindSpore-4/Code11/tree/main/SRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/2023-MindSpore-4/Code11/tree/main/resnet50_adv_pruning
https://github.com/2023-MindSpore-4/Code11/tree/main/retinaface
Video Face Recognition System: RetinaFace-mnet-faster and Secondary Search
https://github.com/2023-MindSpore-4/Code11/tree/main/retinanet_resnet101
https://github.com/2023-MindSpore-4/Code11/tree/main/sphereface
https://github.com/2023-MindSpore-4/Code11/tree/main/ssc_resnet50
https://github.com/2023-MindSpore-4/Code11/tree/main/ssd_resnet_34
https://github.com/2023-MindSpore-4/Code2/tree/main/DeepID
https://github.com/2023-MindSpore-4/Code6/tree/main/PyramidBox
PyramidBox++: High Performance Detector for Finding Tiny Face
https://github.com/2023-MindSpore-4/Code6/tree/main/ReIDStrongBaseline
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/2023-MindSpore-4/Code7/tree/main/SRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/2023-MindSpore-4/Code7/tree/main/STPM
Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofing
https://github.com/2023-MindSpore-4/Code7/tree/main/sphereface
https://github.com/2023-MindSpore-4/Code7/tree/main/squeezenet
https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_resnet34
https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_resnet_34
https://github.com/2023-MindSpore-4/Code8/tree/main/ADNet
ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging
https://github.com/2023-MindSpore-4/Code8/tree/main/cct
https://github.com/2023-MindSpore-4/Code8/tree/main/cdp
Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition
https://github.com/2023-MindSpore-4/Code8/tree/main/centerface
CenterFace: Joint Face Detection and Alignment Using Face as Point
https://github.com/2023-MindSpore-4/Code8/tree/main/centernet_resnet50_v1
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/2023-MindSpore-4/Code9/tree/main/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/2023-MindSpore-4/Code9/tree/main/ResNet
https://github.com/2023-MindSpore-4/Code9/tree/main/RetinaFace_ResNet50
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/2023-MindSpore-4/Code9/tree/main/VGG
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/2024-MindSpore-1/Code4/tree/main/Arcface
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.