https://github.com/YiruS/pointnet_adversarial
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Papers with Code 아카이브
3D Face Reconstruction
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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/Yonder-OSS/D3M-Primitives
https://github.com/YonghaoHe/LFFD-A-Light-and-Fast-Face-Detector-for-Edge-Devices
https://github.com/Young98CN/pointconv_pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/YuanxunLu/LiveSpeechPortraits
Live Speech Portraits: Real-Time Photorealistic Talking-Head Animation
https://github.com/YudeWang/UNet-Satellite-Image-Segmentation
https://github.com/YutingZhang/lmdis-rep
Unsupervised Discovery of Object Landmarks as Structural Representations
https://github.com/Yuto0107/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/YuvalNirkin/face_video_segment
https://github.com/YvanG/VQGAN-CLIP
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/ZBigFish/FER-VT
https://github.com/ZHANG-SHI-CHANG/Pytorch_Retinaface
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/ZTao-z/multiflow-resnet-ssd
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ZVK/Talking-Heads
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
https://github.com/ZVK/talking_heads
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
https://github.com/ZacharyGoshen/cifar-10-classifier
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Zachdr1/CNNs
https://github.com/Ze-Yang/Context-Transformer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Zhachory1/MusicNST
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ZhaoJ9014/face.evoLVe
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/ZhenJie-Zhang/darknet_shoe
https://github.com/ZhihaoZhu/PointNet-Implementation-Tensorflow
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ZhiwenShao/JAANet
Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment
https://github.com/ZmeiGorynych/transformer_pytorch
https://github.com/ZoieMo/Multi-task
FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors
https://github.com/a514514772/hijackgan
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/a736875071/clip-vit-large-patch14
FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age
https://github.com/aakashjhawar/traffic-sign-detection
https://github.com/aannirajpatel/COMP562-Face-Mask-Detect
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/aayushdD/Face_recognition-Keras-openface
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/aba450/Super-Resolution
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/abaietto/neural_ode_classification
https://github.com/abdullahozer11/Segmentation-and-Classification-of-Objects-in-Point-Clouds
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/abel-leulseged/Quick-Draw
https://github.com/abhi1kumar/hrnet_pose_single_gpu
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/abhi2610/ohem
Training Region-based Object Detectors with Online Hard Example Mining
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.