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/MichaelWangfc/distributed-tensorflow-resnet
https://github.com/Mimigatto/face-mask-detection
MaskedFace-Net -- A Dataset of Correctly/Incorrectly Masked Face Images in the Context of COVID-19
https://github.com/Mind23-2/MindCode-101/tree/main/HRNetW48_cls
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/Mind23-2/MindCode-101/tree/main/LightCNN
https://github.com/Mind23-2/MindCode-101/tree/main/MTCNN
Real-world adversarial attack on MTCNN face detection system
https://github.com/Mind23-2/MindCode-101/tree/main/MaskedFaceRecognition
https://github.com/Mind23-2/MindCode-101/tree/main/lresnet100e_ir
https://github.com/Mind23-2/MindCode-112
RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition
https://github.com/Mind23-2/MindCode-119
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Mind23-2/MindCode-13
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/Mind23-2/MindCode-14
CenterFace: Joint Face Detection and Alignment Using Face as Point
https://github.com/Mind23-2/MindCode-174
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/Mind23-2/MindCode-176
https://github.com/Mind23-2/MindCode-3/tree/main/HRNetW48_cls
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/Mind23-2/MindCode-3/tree/main/HRNetW48_seg
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/Mind23-2/MindCode-3/tree/main/HiFaceGAN
HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
https://github.com/Mind23-2/MindCode-3/tree/main/LightCNN
https://github.com/Mind23-2/MindCode-3/tree/main/MTCNN
Real-world adversarial attack on MTCNN face detection system
https://github.com/Mind23-2/MindCode-3/tree/main/MaskedFaceRecognition
https://github.com/Mind23-2/MindCode-3/tree/main/lresnet100e_ir
https://github.com/Mind23-2/MindCode-31
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Mind23-2/MindCode-4/tree/main/hrnet
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/Mind23-2/MindCode-4/tree/main/vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Mind23-2/MindCode-41
https://github.com/Mind23-2/MindCode-5/tree/main/PGAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/Mind23-2/MindCode-5/tree/main/PyramidBox
PyramidBox++: High Performance Detector for Finding Tiny Face
https://github.com/Mind23-2/MindCode-56
https://github.com/Mind23-2/MindCode-59
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/Mind23-2/MindCode-63
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Mind23-2/MindCode-67
https://github.com/Mind23-2/MindCode-70
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/Mind23-2/MindCode-71
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/Mind23-2/MindCode-88/tree/main/sphereface
SphereFace Revived: Unifying Hyperspherical Face Recognition
https://github.com/Mind23-2/MindCode-95
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/MindCode-4/code-11/tree/main/circle-loss
Circle Loss: A Unified Perspective of Pair Similarity Optimization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.