https://github.com/sarthakTUM/roofn3d
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
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/sayakpaul/Revisiting-Pooling-in-CNNs
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
https://github.com/sayannath/Zero-DCE-TFLite
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
https://github.com/sberbank-ai/DigiTales
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/sberbank-ai/DigiTeller
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/sberbank-ai/real-esrgan
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/scoutant/face-blur
https://github.com/sdamolini/LooksLikeWho
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/sdauzcm/sr-basicsr
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/sdll/hrnet-pose-estimation
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/sdu2011/darknet_alexyab
https://github.com/seasonSH/DocFace
https://github.com/sebastiaopamplona/msc-thesis-deepfsim
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/senguptaumd/SfSNet
An All-In-One Convolutional Neural Network for Face Analysis
https://github.com/seongilp/DFE604-2020F-FinalProject
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/serengil/deepface
Deep Learning Face Representation by Joint Identification-Verification
https://github.com/serengil/retinaface
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/sergkuzn148/lol3
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/sergkuzn148/stg
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/setharram/facenet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/sevenHsu/Face_Recognition_IN_Video
https://github.com/shahbuland/basic-gans
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/shahidul56/Tiny_Faces_in_Tensorflow_msc
https://github.com/shanxuanchen/FacialExpressionRecognition
Island Loss for Learning Discriminative Features in Facial Expression Recognition
https://github.com/shariff94/Facial_Keypoint_Detection
Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet
https://github.com/shashigharti/secure-privateai-scholarship-challenge
https://github.com/shenwei1231/caffe-DeepRegressionForests
https://github.com/sherkhan15/distarctor-Detection_Eyes_Emotions_CI
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/shi510/ffem
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/shijianjian/HRNet_Keras
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/shijx12/AR-Tree
https://github.com/shinyingl/CV_Facial_Keypoints
Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet
https://github.com/shiqiyu/libfacedetection.train
https://github.com/shoutOutYangJie/Few-Shot-Adversarial-Learning-for-face-swap
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
https://github.com/shridharrhegde/vgg-age
Deep Convolutional Neural Network for Age Estimation based on VGG-Face Model
https://github.com/shuShelby/main
FaceNet: A Unified Embedding for Face Recognition and Clustering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.