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/1996scarlet/faster-mobile-retinaface
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/1adrianb/2D-and-3D-face-alignment
https://github.com/1adrianb/binary-face-alignment
https://github.com/1adrianb/binary-human-pose-estimation
https://github.com/1adrianb/face-alignment-training
https://github.com/1chenchen22/LFNSB
https://github.com/1ho0jin1/QMUL-SurvFace-Open-Set-Identification
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/ADNet
ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging
https://github.com/2023-MindSpore-1/ms-code-162
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/2023-MindSpore-1/ms-code-178
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/2023-MindSpore-1/ms-code-18/tree/main/LightCNN
https://github.com/2023-MindSpore-1/ms-code-18/tree/main/MaskedFaceRecognition
https://github.com/2023-MindSpore-1/ms-code-201
https://github.com/2023-MindSpore-1/ms-code-211/tree/main/DeepID
https://github.com/2023-MindSpore-1/ms-code-213/tree/main/FaceDetection
https://github.com/2023-MindSpore-1/ms-code-213/tree/main/FaceNet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HMR
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HRNetW48_cls
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HRNetW48_seg
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HiFaceGAN
HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
https://github.com/2023-MindSpore-1/ms-code-215/tree/main/PGAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/PyramidBox
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/retinanet_resnet101
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/retinanet_resnet152
https://github.com/2023-MindSpore-1/ms-code-223/tree/main/RetinaFace_ResNet50
Video Face Recognition System: RetinaFace-mnet-faster and Secondary Search
https://github.com/2023-MindSpore-1/ms-code-223/tree/main/RetinaNet
https://github.com/2023-MindSpore-1/ms-code-224/tree/main/ADNet
ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging
https://github.com/2023-MindSpore-1/ms-code-35
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/2023-MindSpore-1/ms-code-44
https://github.com/2023-MindSpore-1/ms-code-6/tree/main/centerface
CenterFace: Joint Face Detection and Alignment Using Face as Point
https://github.com/2023-MindSpore-1/ms-code-6/tree/main/centernet_resnet50_v1
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/2023-MindSpore-4/Code-5/tree/main/HRNetW48_cls
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/2023-MindSpore-4/Code-5/tree/main/HiFaceGAN
HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
https://github.com/2023-MindSpore-4/Code-5/tree/main/LightCNN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.