Papers with Code 아카이브
3D Face Reconstruction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/hukkelas/progan-pytorch
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/human-analysis/RankGAN
https://github.com/human-analysis/secure-face-matching
https://github.com/humbleOldSage/Triplet-Loss-for-Personal-dataset
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/hustic/TinyImageSet
https://github.com/hustvl/sparseinst
https://github.com/huybik/GAN-Experiments-Pytorch
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/huybik/GANs-Pytorch
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/huyhieupham/3D-Face-Recognition
Learning from Millions of 3D Scans for Large-scale 3D Face Recognition
https://github.com/huytranvan2010/RetinaNet-Understanding
https://github.com/hweersot/darknet-custom
https://github.com/hx19940102/Image-Segmentation-based-on-FCN
https://github.com/hyang0129/foodclassapp
https://github.com/hycis/TensorGraph
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/hyhchaos/FakePolisher
FakePolisher: Making DeepFakes More Detection-Evasive by Shallow Reconstruction
https://github.com/hysts/pytorch_resnet
https://github.com/hysts/pytorch_resnet_preact
https://github.com/hz-ants/pointnet.pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/hz2538/iot_JennyGo
https://github.com/hzh8311/pyhowfar
https://github.com/hzlsaber/sida
https://github.com/i6092467/diff-bias-proxies
https://github.com/iArunava/ResNet
https://github.com/iChenning/facedetection
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/iPRoBe-lab/semi-adversarial-networks
Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images
https://github.com/iacopomasi/face_specific_augm
https://github.com/iamgroot42/A-LINK
https://github.com/ibadr/RetinaFace
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/ibbu10/Facial-Landmark-Detection-Using-CNN
Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet
https://github.com/ibendrup/MulticolumnNetwork
https://github.com/icey-zhang/MTCNN_paddle
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/icey-zhang/RetinaFace_paddle
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/idealo/image-super-resolution
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/iiTzFrankie/DCJT
https://github.com/iigroup/mm-celeba-hq-dataset
TediGAN: Text-Guided Diverse Face Image Generation and Manipulation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.