https://github.com/RenFeiTemp/murauer/tree/master/source/my_net
Learning Pose Specific Representations by Predicting Different Views
Papers with Code 아카이브
Hand Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Hand pose estimation is the task of finding the joints of the hand from an image or set of video frames.
<span style="color:grey; opacity: 0.6">( Image credit: Pose-REN )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/RonLek/FastV2C-HandNet
DeepPrior++: Improving Fast and Accurate 3D Hand Pose Estimation
https://github.com/Wavelet303/HAMR
https://github.com/YangYangTaoTao/V2V-PoseNet_Pytorch
https://github.com/adnanalam53/cycleGAN
Learning from Simulated and Unsupervised Images through Adversarial Training
https://github.com/ajdillhoff/colorhandpose3d-pytorch
https://github.com/ajdillhoff/simgan-pytorch
Learning from Simulated and Unsupervised Images through Adversarial Training
https://github.com/akanazawa/hmr
https://github.com/anilarmagan/HANDS19-Challenge-Toolbox
https://github.com/ashkanpakzad/atn
Learning from Simulated and Unsupervised Images through Adversarial Training
https://github.com/balala8/FastViT_pytorch
FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization
https://github.com/bnestor/second-hand-rl
https://github.com/bo-zhang-cs/CACNet-Pytorch
A2J: Anchor-to-Joint Regression Network for 3D Articulated Pose Estimation from a Single Depth Image
https://github.com/boukhayma/3dhand
https://github.com/carpedm20/simulated-unsupervised-tensorflow
Learning from Simulated and Unsupervised Images through Adversarial Training
https://github.com/dragonbook/V2V-PoseNet-pytorch
https://github.com/dumyy/handpose
DeepPrior++: Improving Fast and Accurate 3D Hand Pose Estimation
https://github.com/facebookresearch/InterHand2.6M
https://github.com/facebookresearch/assemblyhands-toolkit
AssemblyHands: Towards Egocentric Activity Understanding via 3D Hand Pose Estimation
https://github.com/geopavlakos/hamer
https://github.com/grgward108/PosePretrain
A Unified Transformer-Based Framework with Pretraining For Whole Body Grasping Motion Generation
https://github.com/guiggh/hand_pose_action
First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations
https://github.com/hanchenchen/hand-complete-w-Handtailor
HandTailor: Towards High-Precision Monocular 3D Hand Recovery
https://github.com/hassony2/manopth
Learning joint reconstruction of hands and manipulated objects
https://github.com/hassony2/obman
Learning joint reconstruction of hands and manipulated objects
https://github.com/hassony2/obman_train
Learning joint reconstruction of hands and manipulated objects
https://github.com/hsp-iit/byogg
Bring Your Own Grasp Generator: Leveraging Robot Grasp Generation for Prosthetic Grasping
https://github.com/hxwork/H2ONet_Pytorch
https://github.com/jsll/pytorch_6dof-graspnet
6-DOF GraspNet: Variational Grasp Generation for Object Manipulation
https://github.com/judyye/ihoi
What's in your hands? 3D Reconstruction of Generic Objects in Hands
https://github.com/karanshahgithub/CS256-AI-Pose2Mesh
Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/fastvit
FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization
https://github.com/lzfff12/hfl-net
https://github.com/mhruz/POTR
https://github.com/mjdietzx/SimGAN
Learning from Simulated and Unsupervised Images through Adversarial Training
https://github.com/nanzhaogang/contrib/tree/master/application/weakly-supervised-mesh-convolutional-hand-reconstruction-in-the-wild
Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.