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/mabhay3420/Deep-Into-CNN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/machanic/AU_R-CNN
AU R-CNN: Encoding Expert Prior Knowledge into R-CNN for Action Unit Detection
https://github.com/maciejdomagala/DLWP_notes
https://github.com/mackitron/COVID19
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/madhavambati/Face-Recognition
https://github.com/maggie0106/Graph-CNN-in-3D-Point-Cloud-Classification
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/magicsunday/pico
Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
https://github.com/magnificent1208/r3det-on-mmdetection
https://github.com/mahathi26/ciss
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/mahmoodlab/hest
https://github.com/mahxn0/RetinaFace_For_Face_Plate_Detect
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/makatx/YOLO_ResNet
https://github.com/malin9402/2020-0221
https://github.com/malin9402/arcface
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/malin9402/retiface
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/manankshastri/ART
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/manishravula/yolov3_tiny_pruned
https://github.com/manoranjan03/PaperImplementations
https://github.com/mapooon/PetFace
PetFace: A Large-Scale Dataset and Benchmark for Animal Identification
https://github.com/maragori/DeepfakeForensics-v1
https://github.com/marcin-laskowski/Pulse
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/marcuspearce/MP_FairFace
FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age
https://github.com/marload/ConvNets-TensorFlow2
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/marmara-machine-learning/Facial_Keypoints_NaimishNet_GPU
Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet
https://github.com/martin220485/everybody_dance_now_pytorch
https://github.com/masoudnick/brain-tumor-mri-classification
https://github.com/matakshay/Neural_Image_Caption_Generator
https://github.com/mathieuorhan/darknet
https://github.com/matlab-deep-learning/mtcnn-face-detection
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/matt-roz/progressive_growing_of_gans_tensorflow_2
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/maxmax1992/WGAN_WGAN-GP
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/maxwelltsai/DeepGalaxy
https://github.com/mayureshpatil30/test-image
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/mbsariyildiz/resnet-pytorch
https://github.com/mcbuehler/DeepSEE
DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.