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/DableUTeeF/keras-efficientnet
https://github.com/Daeijavad/MAD-GAN
https://github.com/DaikiTanak/manifold_mixup
https://github.com/Daisy-Zhang/Face-X-ray
https://github.com/DaloroAT/first_break_picking
https://github.com/DaneyAlex5/Live-Face-Verification-Using-Deep-Learning
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/DaneyAlex5/Webcam-based-Face-Recognition-using-Deep-Learning-
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/Danil328/RetinaFace
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/DataScienceNigeria/Fake-Detection-dataset-for-deepfake-from-Google-and-Jigsaw
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/DeadAt0m/ActiveSparseShifts-PyTorch
Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions
https://github.com/Deci-AI/super-gradients
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/DeepMark/deepmark
https://github.com/Denver-Pinto/FaceRecognitionAndroid
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/DetectionTeamUCAS/RetinaNet_Tensorflow_Rotation
https://github.com/Dictator890/ESRGAN-Android-Demo
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/Dijaq/pain_regression
Regularizing Face Verification Nets For Pain Intensity Regression
https://github.com/DingXiaoH/DiverseBranchBlock
RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition
https://github.com/DingXiaoH/GSM-SGD
RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition
https://github.com/DingXiaoH/ResRep
RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition
https://github.com/Dir-b/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/DivJAth/DeepLearning5922
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/DogeZen/resnet2onnx2tensorrt
https://github.com/DonCorle0ne/damage_detection
https://github.com/DongDem/RangeLoss_MarginalLoss_Tensorflow
https://github.com/DongDem/RangeLoss_Marginal_Loss_Tensorflow
https://github.com/Dou-Yu-xuan/FSRNet
FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors
https://github.com/Dou-Yu-xuan/vggface2-pytorch
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/Doyosae/Deep-Complex-Networks
https://github.com/Doyosae/Deep_Complex_Networks
https://github.com/DrMMZ/RetinaNet
https://github.com/DrRyanHuang/VGGNet
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Duplums/bhb10k-dl-benchmark
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/DylanWusee/pointconv_pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ECE685-FinalProject/3D-object-recognition
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/EMsnap/RobotSorting
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.