https://github.com/deepmind/kinetics-i3d
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Papers with Code 아카이브
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/dlpbc/keras-kinetics-i3d
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
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/google-deepmind/kinetics-i3d
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/greenlet/hand_segment
HGR-Net: A Fusion Network for Hand Gesture Segmentation and Recognition
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/guillaumephd/deep_learning_hand_gesture_recognition
https://github.com/haamoon/mmtm
https://github.com/hanchenchen/hand-complete-w-Handtailor
HandTailor: Towards High-Precision Monocular 3D Hand Recovery
https://github.com/hassony2/kinetics_i3d_pytorch
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
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/helloxy96/CS5242_Project2020
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
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/ivanwilliammd/I3DR-Net-Transfer-Learning
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/jerryljq/ActionRecognition
Two-Stream Convolutional Networks for Action Recognition in Videos
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/k-muhibul/Pattern_hand-gesture
Real-time Hand Gesture Detection and Classification Using Convolutional Neural Networks
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/liujf69/TD-GCN-Gesture
https://github.com/lzfff12/hfl-net
https://github.com/mHealthBuet/SegCodeNet
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/matyasbohacek/spoter
https://github.com/mcgridles/LENS
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/metalbubble/TRN-pytorch
https://github.com/mhruz/POTR
https://github.com/midusi/cacic2019-handshapes
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.