Face Alignment
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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/1adrianb/face-alignment
공식 코드https://github.com/1adrianb/unsupervised-face-representation
공식 코드Pre-training strategies and datasets for facial representation learning
https://github.com/AaronJackson/vrn
공식 코드Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression
https://github.com/D-X-Y/SAN
공식 코드https://github.com/DeokyunKim/Progressive-Face-Super-Resolution
공식 코드Progressive Face Super-Resolution via Attention to Facial Landmark
https://github.com/FacePerceiver/FaRL
공식 코드General Facial Representation Learning in a Visual-Linguistic Manner
https://github.com/LeiJiangJNU/DAMDNet
공식 코드Dual Attention MobDenseNet(DAMDNet) for Robust 3D Face Alignment
https://github.com/MCG-NJU/SADRNet
공식 코드https://github.com/MarekKowalski/DeepAlignmentNetwork
공식 코드Deep Alignment Network: A convolutional neural network for robust face alignment
https://github.com/MarekKowalski/HoloFace
공식 코드HoloFace: Augmenting Human-to-Human Interactions on HoloLens
https://github.com/Pea-Shooter/ADNet
공식 코드ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging
https://github.com/YadiraF/DECA
공식 코드Learning an Animatable Detailed 3D Face Model from In-The-Wild Images
https://github.com/YadiraF/PRNet
공식 코드Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
https://github.com/ZhaoJ9014/face.evoLVe.PyTorch
공식 코드https://github.com/ZhiwenShao/MCNet-Extension
공식 코드https://github.com/ZhiwenShao/PyTorch-JAANet
공식 코드J$\hat{\text{A}}$A-Net: Joint Facial Action Unit Detection and Face Alignment via Adaptive Attention
https://github.com/abhi1kumar/LUVLi
공식 코드LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood
https://github.com/aliprf/ASMNet
공식 코드ASMNet: a Lightweight Deep Neural Network for Face Alignment and Pose Estimation
https://github.com/aliprf/acr-loss
공식 코드ACR Loss: Adaptive Coordinate-based Regression Loss for Face Alignment
https://github.com/aliprf/kd-loss
공식 코드Facial Landmark Points Detection Using Knowledge Distillation-Based Neural Networks
https://github.com/andresprados/spiga
공식 코드Shape Preserving Facial Landmarks with Graph Attention Networks
https://github.com/anuragranj/coma
공식 코드https://github.com/baoshengyu/H3R
공식 코드https://github.com/bghojogh/Pixel-Aligned-Face-Recognition
공식 코드Pixel-Level Alignment of Facial Images for High Accuracy Recognition Using Ensemble of Patches
https://github.com/bobetocalo/bobetocalo_pami20
공식 코드https://github.com/browatbn2/3FabRec
공식 코드https://github.com/choyingw/SynergyNet
공식 코드Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry
https://github.com/cleardusk/3DDFA
공식 코드https://github.com/cleardusk/3DDFA_V2
공식 코드https://github.com/cyj907/unsupervised-detail-layer
공식 코드https://github.com/deepinsight/insightface
공식 코드Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment
https://github.com/ebekkers/gsplinets
공식 코드https://github.com/fengju514/Face-Pose-Net
공식 코드https://github.com/fudandisc/socioverse
공식 코드https://github.com/germino-liuhe/dsat
공식 코드Precise Facial Landmark Detection by Dynamic Semantic Aggregation Transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.