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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/songquanpeng/BlendGAN
BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation
https://github.com/soubhiksanyal/FLAME_PyTorch
https://github.com/soubhiksanyal/RingNet
Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
https://github.com/soubhiksanyal/now_evaluation
Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
https://github.com/soumik12345/Nearest-Celebrity-Face
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/soumik12345/Zero-DCE
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
https://github.com/soumik12345/point-cloud-segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/soumith/convnet-benchmarks
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/souravverma94/pre_trained_imgclassifier_dogBreeds
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sovit-123/Deep-Learning-Image-Captioning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sppsps/VGG-16-classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/srihari-humbarwadi/retinanet-tensorflow2.x
https://github.com/srihari-humbarwadi/tensorflow_retinanet
https://github.com/srilakshmiv14/FaceNet_Pytorch
https://github.com/srvcodes/clap4clip
https://github.com/starhiking/ATF
https://github.com/stdereka/knowledge-distillation
An Embarrassingly Simple Approach for Knowledge Distillation
https://github.com/stefkim/stylegan-batik
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/stergioc/smooth-transformer
Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance
https://github.com/stevehansen43/progressive_GAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/stevenzhou2017/darknet_AlexeyAB
https://github.com/stresearch/xfr
https://github.com/strivebo/image_segmentation_dl
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/sudhanshu2k/Signature-Verification
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/sudharavali/objectDetectionYOLO
https://github.com/suhas1999/Flip-kart-grid-challenge
https://github.com/suhuijia/pggan
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/sukibean163/RetinaFace_Paddle
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/sulasen/race-events-recognition-1
https://github.com/sumathi16/FaceRecognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/sumitkutty/Anti-Spoof-Face-Recognition
https://github.com/summerice9/AniGAN
AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation
https://github.com/sungwool/virtual_softmax
https://github.com/sungyoonahn/Hint-based-image-colorization-using-Attention-Unet
https://github.com/sunil-rival/insightface
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.