Papers with Code 아카이브
3D Face Reconstruction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 9
- 보존된 평가 기록
- 94
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/yakhyo/retinaface-pytorch
https://github.com/yangboz/TransferLearning4Dentist
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/yangorwell/NGPlus
https://github.com/yangyi02/densebox
DenseBox: Unifying Landmark Localization with End to End Object Detection
https://github.com/yangyucheng000/-insightface
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/yangyucheng000/ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/yangyucheng000/HRNetW48_cls
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/yangyucheng000/MSpaper/tree/main/U-GAT-IT
https://github.com/yangyucheng000/Paper-2/tree/main/ARCM-main
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/yangyucheng000/Paper-4/tree/main/JLONMFSC
Optimal Projections for Discriminative Dictionary Learning using the JL-lemma
https://github.com/yangyucheng000/Paper-4/tree/main/LADS-mindspore
LAD-RCNN:A Powerful Tool for Livestock Face Detection and Normalization
https://github.com/yangyucheng000/Paper-4/tree/main/PDOC_mindspore
SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition
https://github.com/yangyucheng000/RetinaFace
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/yangyucheng000/paper
CenterFace: Joint Face Detection and Alignment Using Face as Point
https://github.com/yangyucheng000/papercode-2/tree/main/JLONMFSC-master
Optimal Projections for Discriminative Dictionary Learning using the JL-lemma
https://github.com/yangyucheng000/papercode-2/tree/main/LADS-mindspore
LAD-RCNN:A Powerful Tool for Livestock Face Detection and Normalization
https://github.com/yangyucheng000/papercode-2/tree/main/SFAT-MindSpore-main
https://github.com/yangyucheng000/papercode-2/tree/main/TAA_MindSpore-main
TAA-GCN: A Temporally Aware Adaptive Graph Convolutional Network for Age Estimation
https://github.com/yangyucheng000/pfld
https://github.com/yangyucheng000/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/yangyucheng000/resnet_Ascend
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/yashclone999/ResNet_MODEL
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/ycao5602/SAL
https://github.com/ycxioooong/MovieSynopsisAssociation
Unifying Identification and Context Learning for Person Recognition
https://github.com/yczhang1017/SSD_resnet_pytorch
Very Deep Convolutional Networks for Large-Scale Image 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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.