https://github.com/J-woooo/Mask_Remover
Progressive Growing of GANs for Improved Quality, Stability, and Variation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Progressive Growing of GANs for Improved Quality, Stability, and Variation
FaceNet: A Unified Embedding for Face Recognition and Clustering
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
Deep High-Resolution Representation Learning for Visual Recognition
High-Resolution Representations for Labeling Pixels and Regions
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Deep Alignment Network: A convolutional neural network for robust face alignment
RetinaFace: Single-stage Dense Face Localisation in the Wild
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
FaceNet: A Unified Embedding for Face Recognition and Clustering
Deep Imbalanced Learning for Face Recognition and Attribute Prediction
Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet
Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network
FaceNet: A Unified Embedding for Face Recognition and Clustering
FDFtNet: Facing Off Fake Images using Fake Detection Fine-tuning Network
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Age-Net: An MRI-Based Iterative Framework for Brain Biological Age Estimation
FaceNet: A Unified Embedding for Face Recognition and Clustering
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
FaceNet: A Unified Embedding for Face Recognition and Clustering
Occlusion Robust Face Recognition Based on Mask Learning with PairwiseDifferential Siamese Network
SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
FaceNet: A Unified Embedding for Face Recognition and Clustering
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.