https://github.com/facundoq/transformational_measures_experiments
Very Deep Convolutional Networks for Large-Scale Image Recognition
Papers with Code 아카이브
3D Face Reconstruction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 9
- 보존된 평가 기록
- 94
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/faddey-w/face-swap
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
https://github.com/fangzong12/FeatherNet
FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofing
https://github.com/faridSam/srn
Selective Refinement Network for High Performance Face Detection
https://github.com/farinamatteo/zero
https://github.com/fastai/fastai
https://github.com/fbplab/MEBeauty-database
https://github.com/fchollet/deep-learning-models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/feidfoe/AdjustBnd4Imbalance
https://github.com/feiyunzhang/person_attribute_mxnet
MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes
https://github.com/felipeyanez/nmf
Primal-Dual Algorithms for Non-negative Matrix Factorization with the Kullback-Leibler Divergence
https://github.com/felixgwu/img_classification_pk_pytorch
https://github.com/fenghansen/ESRGAN-Keras
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/fenglian425/Agriculture_AI
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/fengredrum/cnn-xla
https://github.com/ffletcherr/face-recognition-liveness
Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection
https://github.com/ffr4nz/UnknownUnknowns
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/final-0/Real-ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/final-0/Real-ESRGAN-bicubic
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/fizyr/keras-retinanet
https://github.com/flora-zyx/SJNet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/flynn-chen/faceforensics_benchmark
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/flytocc/PaddleClas
https://github.com/fnyanez/nmf
Primal-Dual Algorithms for Non-negative Matrix Factorization with the Kullback-Leibler Divergence
https://github.com/foamliu/Gaze-Estimation
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/foamliu/InsightFace-PyTorch
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/foamliu/InsightFace-v2
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/foamliu/InsightFace-v3
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/foamliu/MobileFaceNet
MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices
https://github.com/foivospar/Arc2Face
https://github.com/foxer9developer/Face-Emotion-to-Emoji-Convertor
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/fpleoni/its_all_in_the_family
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/francislata/Sign-Language-Digits-CNN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/freddieee/pn_6d_single
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ftdlyc/pointnet_pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/fulviomascara/pytorch-cv
Facial Expression Recognition Research Based on Deep Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.