Papers with Code 아카이브
Gaze Estimation
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Gaze Estimation is a task to predict where a person is looking at given the person’s full face. The task contains two directions: 3-D gaze vector and 2-D gaze position estimation. 3-D gaze vector estimation is to predict the gaze vector, which is usually used in the automotive safety. 2-D gaze position estimation is to predict the horizontal and vertical coordinates on a 2-D screen, which allows utilizing gaze point to control a cursor for human-machine interaction.
Source: A Generalized and Robust Method Towards Practical Gaze Estimation on Smart Phone
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MindSpore-paper-code-3/code7/tree/main/FaceRecognitionForTracking
https://github.com/MindSpore-paper-code-3/code8/tree/main/FaceRecognitionForTracking
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/Tobias-Fischer/rt_gene
https://github.com/VIPL-TAL-GAZE/GAZE2021
https://github.com/adnanalam53/cycleGAN
Learning from Simulated and Unsupervised Images through Adversarial Training
https://github.com/ahmednull/l2cs-net
L2CS-Net: Fine-Grained Gaze Estimation in Unconstrained Environments
https://github.com/ajdillhoff/simgan-pytorch
Learning from Simulated and Unsupervised Images through Adversarial Training
https://github.com/ashkanpakzad/atn
Learning from Simulated and Unsupervised Images through Adversarial Training
https://github.com/carpedm20/simulated-unsupervised-tensorflow
Learning from Simulated and Unsupervised Images through Adversarial Training
https://github.com/chengpengchen/repghost
https://github.com/chenyeheng/SmartCar
https://github.com/chenyeheng/SmartCar-FaceRec
https://github.com/chenyeheng/SmartCar-FaceRecognition
https://github.com/code-implementation1/Code9/tree/main/CRNN
Recurrent CNN for 3D Gaze Estimation using Appearance and Shape Cues
https://github.com/crisie/CRNN-Gaze
Recurrent CNN for 3D Gaze Estimation using Appearance and Shape Cues
https://github.com/dongzelian/multi-view-gaze
https://github.com/dreamer-1996/eye-tracking-with-resnet
https://github.com/erkil1452/gaze360
Gaze360: Physically Unconstrained Gaze Estimation in the Wild
https://github.com/federicopozzi33/MobileOne-PyTorch
https://github.com/frgfm/Holocron
https://github.com/hugochan/Eye-Tracker
https://github.com/hysts/pytorch_mpiigaze
https://github.com/iszff/AGE-Net
https://github.com/james77777778/keras-image-models
https://github.com/kenkyusha/eyeGazeToScreen
https://github.com/kishansharma3012/Deep_Head_Pose_Estimation
https://github.com/kroniidvul/mpiigaze_project
https://github.com/mjdietzx/SimGAN
Learning from Simulated and Unsupervised Images through Adversarial Training
https://github.com/pbonazzi/retina
TinyTracker: Ultra-Fast and Ultra-Low-Power Edge Vision In-Sensor for Gaze Estimation
https://github.com/pbonazzi/tinytracker
TinyTracker: Ultra-Fast and Ultra-Low-Power Edge Vision In-Sensor for Gaze Estimation
https://github.com/pedrobvidal/insightface
3DGazeNet: Generalizing Gaze Estimation with Weak-Supervision from Synthetic Views
https://github.com/rickyhan/SimGAN-Captcha
Learning from Simulated and Unsupervised Images through Adversarial Training
https://github.com/rvorias/uvHolographics
Learning from Simulated and Unsupervised Images through Adversarial Training
https://github.com/shinseung428/simGAN_NYU_Hand
Learning from Simulated and Unsupervised Images through Adversarial Training
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.