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/ChangweiZhang/Awesome-WindowsML-ONNX-Models
https://github.com/CharlesPikachu/CharlesFace
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/CheesyB/cpointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ChiCheng123/SRN
Selective Refinement Network for High Performance Face Detection
https://github.com/ChristianMarzahl/ObjectDetection
https://github.com/Cloud-Computing-IoT/Cloud-Enabled-Smart-Speaker
https://github.com/Cloud-Computing-IoT/speakEasy
https://github.com/Cmeijiang/PointNet_MS
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/CocoSungMin/Gachon_SW_Colorization_Contest
https://github.com/CodeChefVIT/VOID
https://github.com/CoinCheung/pytorch-loss
Partial FC: Training 10 Million Identities on a Single Machine
https://github.com/CompVis/metric-learning-divide-and-conquer
https://github.com/CompVis/unsupervised-part-segmentation
Unsupervised Part-Based Disentangling of Object Shape and Appearance
https://github.com/Conero007/Emotion-Detection-Pytorch
Challenges in Representation Learning: A report on three machine learning contests
https://github.com/CorentinMAG/SRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/CorneliusHsiao/FoodMethodGAN
https://github.com/CrossEntropy/Gesture-images-recognition-ResidualNetwork
https://github.com/CryptoSalamander/pytorch_paper_implementation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/CycloneBoy/PPDetectionPytorch
https://github.com/D-X-Y/ResNeXt-DenseNet
https://github.com/DChaushev/Age-Gender-Recognition
https://github.com/DELTA37/LatentKeypointGAN
https://github.com/DILiS-lab/drivers-of-predictive-aleatoric-uncertainty
FP-Age: Leveraging Face Parsing Attention for Facial Age Estimation in the Wild
https://github.com/DInesh1234-wq/tyredataset
https://github.com/DMarwan/NutricIA
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/DSL-FIQA/DSL-FIQA
https://github.com/DableUTeeF/keras-efficientnet
https://github.com/Daeijavad/MAD-GAN
https://github.com/DaikiTanak/manifold_mixup
https://github.com/Daisy-Zhang/Face-X-ray
https://github.com/DaloroAT/first_break_picking
https://github.com/DaneyAlex5/Live-Face-Verification-Using-Deep-Learning
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/DaneyAlex5/Webcam-based-Face-Recognition-using-Deep-Learning-
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/Danil328/RetinaFace
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/DataScienceNigeria/Fake-Detection-dataset-for-deepfake-from-Google-and-Jigsaw
FaceForensics++: Learning to Detect Manipulated Facial Images
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.