https://github.com/cedricoeldorf/ConditionalStyleGAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
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/celsolbm/Attention_Text
https://github.com/celsolbm/imdb_sentiment_experiments
https://github.com/cem8301/EmotionDetector
https://github.com/cfl0122/face
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/chamikara1986/BA-BAM
https://github.com/changsukim-ku/order-learning
https://github.com/charlesq34/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/chayanchatterjee/cbc-skynet
https://github.com/chayanchatterjee/gw-skylocator
https://github.com/chelvanai/Oneshot-Face-Identify
https://github.com/chengcv/J3S
Sparse Coding and Dictionary Learning for Symmetric Positive Definite Matrices: A Kernel Approach
https://github.com/chenggongliang/arcface
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
https://github.com/chenqiang19/vgg_real_app_age_regression
On the effect of age perception biases for real age regression
https://github.com/chenyeheng/SmartCar
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/chenyeheng/SmartCar-FaceRec
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/chenyeheng/SmartCar-FaceRecognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/chinakook/hr101_mxnet
https://github.com/chinhau-lim/fer2013
Challenges in Representation Learning: A report on three machine learning contests
https://github.com/chinhau-lim/fer_2013
Challenges in Representation Learning: A report on three machine learning contests
https://github.com/chldydgh4687/2020-1.VideoCaptioning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/chosj95/PFPNet.pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/choyingw/GD-HASLR
Occluded Face Recognition Using Low-rank Regression with Generalized Gradient Direction
https://github.com/chrhenning/hypercl
https://github.com/chrisqqq123/FA-Dist-EfficientNet
https://github.com/christy1206/STSTNet
Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition
https://github.com/chroniccrash/facestuff
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/cindyloo/rockstar-ai
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/civilman628/pgan
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/cj-mclaughlin/segmentation_research
https://github.com/cjfghk5697/Pytorch-Research-Paper-Implementations
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ck37/coral-ordinal
Rank consistent ordinal regression for neural networks with application to age estimation
https://github.com/ckapoor7/DeXpression
DeXpression: Deep Convolutional Neural Network for Expression Recognition
https://github.com/clairehester/face-mask-detector
Deep Learning Framework to Detect Face Masks from Video Footage
https://github.com/claushofmann/GAN-game-art
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/clcarwin/sphereface
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.