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.
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Progressive Growing of GANs for Improved Quality, Stability, and Variation
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https://github.com/umd-fire-coml/2020-Face-Generation
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Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
https://github.com/ung200/thats-what-obama-said
https://github.com/unsky/RetinaNet
https://github.com/unsky/focal-loss
https://github.com/usnistgov/image-classification-resnet50
https://github.com/usnistgov/image-regression-resnet50
https://github.com/ut-vision/rot-mvgaze
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https://github.com/utsawk/CarND-Traffic-Sign-Classifier-Project
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Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/varshaneya/Res-SE-Net
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https://github.com/vayuvegula/kerasModels
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https://github.com/vgangal101/VGG_models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/vgangal101/resnet_models
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Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution
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Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/viebboy/MultilinearCompressiveLearningWithPrior
https://github.com/vietanhdev/open-adas
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Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/vikramkarthikeyan/Face-R-FCN
Detecting Faces Using Region-based Fully Convolutional Networks
https://github.com/vill-lab/2024-aaai-hpt
https://github.com/vinairesearch/lemul
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출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.