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/nsom/Basic-Models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nsom/VGG16
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ntrin/CIFAR_10_ML
https://github.com/nu11us/WC-MS-Celeb
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
https://github.com/nveldt/LamCC
https://github.com/oarriaga/paz
https://github.com/oawiles/FAb-Net
Self-supervised learning of a facial attribute embedding from video
https://github.com/obj2vec/obj2vec
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/oellop/facial_expression
Challenges in Representation Learning: A report on three machine learning contests
https://github.com/okeer/nst-tele-bot
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/oktapodi/ps_resNet
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/olaviinha/NeuralImageSuperResolution
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/olegpolivin/antisleep
https://github.com/olgi9911/MMDG
Suppress and Rebalance: Towards Generalized Multi-Modal Face Anti-Spoofing
https://github.com/ollewelin/libtorch-GPU-CNN-test-MNIST-with-Batchnorm
https://github.com/omaarelsherif/Face-Detection-Using-Deep-Learning
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/omaarelsherif/Face-Detection-Using-MTCNN
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/omarsayed7/Deep-Emotion
Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network
https://github.com/omidmn/Engagement-Recognition
Automatic Recognition of Student Engagement using Deep Learning and Facial Expression
https://github.com/omidnezami/Engagement-Recognition
Automatic Recognition of Student Engagement using Deep Learning and Facial Expression
https://github.com/omkarghugarkar007/Neural_Super_Sampling
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/omrijsharon/torchlex
https://github.com/omron-sinicx/sbsfigures
PetFace: A Large-Scale Dataset and Benchmark for Animal Identification
https://github.com/ongyongzheng/generative_encoder
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/onion-liu/aahq-dataset
BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation
https://github.com/onwan946/DeepFaceLab
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/oorrppp2/darknet-pose
https://github.com/opeco17/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/open-mmlab/mmclassification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/open-mmlab/mmdetection
https://github.com/open-mmlab/mmgeneration
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/open-mmlab/mmpose
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/open-mmlab/mmsegmentation
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/opencv/opencv_zoo/tree/master/models/face_recognition_sface
SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition
https://github.com/oskyhn/CNNs-Without-Borders
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.