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/rnradon/gender_emotion_classification
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/robertofranceschi/Image-classification-on-Caltech101-using-CNNs
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/rockstar-0000/lip_sync_test
A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild
https://github.com/rohanbhutani1993/GeneratingDeepfakes
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/rohansuresh27/FaceNet_face_verification
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/rohith2506/facedetect
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/rohithv/Grammatical-facial-expression-recognition
Grammatical facial expression recognition using customized deep neural network architecture
https://github.com/romaintha/pytorch_pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ronghuaiyang/arcface-pytorch
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/ronny3050/AdvFaces
https://github.com/roomylee/self-attentive-emb-tf
https://github.com/roscibely/gender-prediction
Predicting Gender by First Name Using Character-level Machine Learning
https://github.com/roshankoirala/Fire_Detection_Model
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/rosinality/progressive-gan-pytorch
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/rowerliu/adbd
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/rowerliu/add
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/roysai/Humpback-whale-identity-challenge
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/rtanno21609/AdaptiveNeuralTrees
https://github.com/ruidaniel/rbaca
https://github.com/ruinmessi/RFBNet
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/run-youngjoo/SC-FEGAN
SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
https://github.com/rupaai/60DaysOfUdacity
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/ry/tensorflow-resnet
https://github.com/ryanwebster90/gen-overfitting-latent-recovery
Detecting Overfitting of Deep Generative Networks via Latent Recovery
https://github.com/ryubidragonfire/mydarknet
https://github.com/s3nh/unet-midv500
https://github.com/saaries/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/saber2011/darknet
https://github.com/saednasir/Helmet_Yolo
https://github.com/sagnik1511/Transfer-Learning-with-Python
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sagpant/3dmm_cnn
Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network
https://github.com/sahilunagar/VGG16-model
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/saiamrit/Facenet-pytorch
https://github.com/saic-fi/bayesian-prompt-learning
https://github.com/saikrishnadas/ResNet-Pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.