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/Godot-yry/VGG16_mindspore
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Gojay001/toolkit-DeepLearning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Gowti-AiboT/GAN-based-SISR-Keras
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/Graylab/deepH3-distances-orientations
https://github.com/Gryffindor112358/Arcface
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/Guillem96/efficient-net-tf
https://github.com/Guillem96/efficientdet-tf
https://github.com/GuoQuanhao/RetinaFace-Paddle
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/GuoQuanhao/Tiny-Face-Paddle
https://github.com/GuohongLi/face-alignment-pytorch
https://github.com/GurpartapSS/Realtime_FaceRecognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/HHTseng/video-classification
https://github.com/HKUST-NISL/Multitask-Emotion-Recognition-with-Incomplete-Labels
https://github.com/HRNet/HRNet-FCOS
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/HRNet/HRNet-Facial-Landmark-Detection
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/HRNet/HRNet-Human-Pose-Estimation
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/HRNet/HRNet-Image-Classification
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/HRNet/HRNet-MaskRCNN-Benchmark
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/HRNet/HRNet-Object-Detection
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/HRNet/HRNet-Semantic-Segmentation
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/Halesu/4th-ML100Days
https://github.com/HamadYA/GhostFaceNets
https://github.com/Hamiedamr/EmotionNet-Nano-An-Efficient-Deep-Convolutional-Neural-Network-Design-for-Real-time-Facial-Expressi
https://github.com/Hamiltonsjtu/based_on_facenet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/Hangz-nju-cuhk/Talking-Face-Generation-DAVS
Talking Face Generation by Adversarially Disentangled Audio-Visual Representation
https://github.com/HannH/DMFN
https://github.com/HarmanDotpy/Normalizations-in-Deep-Learning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Harry-Muzart/harry-muzart.github.io
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Harut0726/my-pointnet-tensorflow
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/HavenFeng/photometric_optimization
https://github.com/Hayashi-Yudai/ML_models
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/Hazqeel09/ellzaf_ml
S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens
https://github.com/HearyShen/HAN-SelfAttentive
https://github.com/Heonozis/FaceShifter-pytorch
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
https://github.com/HiJiGOO/self-attentive
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.