Papers with Code 아카이브
Face Alignment
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 26
- 보존된 평가 기록
- 213
Face alignment is the task of identifying the geometric structure of faces in digital images, and attempting to obtain a canonical alignment of the face based on translation, scale, and rotation.
<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/Team-Neighborhood/awesome-face-detection
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/TimoBolkart/BFM_to_FLAME
https://github.com/TimoBolkart/FLAME-Blender-Add-on
https://github.com/TimoBolkart/FLAME-Universe
https://github.com/TimoBolkart/TF_FLAME
https://github.com/TropComplique/mtcnn-pytorch
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/WangChen0902/MTCNN-Paddle
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/Weijian-li/unsupervised_inter_intra_landmark
Structured Landmark Detection via Topology-Adapting Deep Graph Learning
https://github.com/XgTu/2DASL
3D Face Reconstruction from A Single Image Assisted by 2D Face Images in the Wild
https://github.com/XgTu/2DASL-CNN
3D Face Reconstruction from A Single Image Assisted by 2D Face Images in the Wild
https://github.com/ZhiwenShao/JAANet
Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment
https://github.com/ZoieMo/Multi-task
FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors
https://github.com/aannirajpatel/COMP562-Face-Mask-Detect
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/abhi1kumar/hrnet_pose_single_gpu
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/aejion/neuface
ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representations
https://github.com/affromero/SMILE
Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression
https://github.com/ainrichman/Peppa-Facial-Landmark-PyTorch
https://github.com/alexattia/ExtendedTinyFaces
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/alililia/ascend_HRNetW48_cls
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/anshky/HR-NET
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/arsfutura/face-recognition
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/baoshengyu/deep-high-resolution-net.pytorch
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/bielcarvalho/RPII-EP
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/bobetocalo/bobetocalo_eccv18
Face Alignment using a 3D Deeply-initialized Ensemble of Regression Trees
https://github.com/bobetocalo/bobetocalo_prl19
https://github.com/chenyeheng/SmartCar
https://github.com/chenyeheng/SmartCar-FaceRec
https://github.com/chenyeheng/SmartCar-FaceRecognition
https://github.com/cs-giung/FSRNet-pytorch
FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors
https://github.com/cydonia999/VGGFace2-pytorch
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/d-shivam/Pose-estimation-based-action-recognition-for-help-Situation-Identification
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/deepinx/SDU_face_alignment
Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment
https://github.com/deepinx/sdu-face-alignment
Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment
https://github.com/deftruth/torchlm
Pixel-in-Pixel Net: Towards Efficient Facial Landmark Detection in the Wild
https://github.com/dengpingfan/fsgan
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.