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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/apple/ml-fastvit
공식 코드FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization
https://github.com/ataboukhadra/thor-net
공식 코드https://github.com/bardiadoosti/HOPE
공식 코드HOPE-Net: A Graph-based Model for Hand-Object Pose Estimation
https://github.com/byeongkeun-kang/HOI-dataset
공식 코드Hand Segmentation for Hand-Object Interaction from Depth map
https://github.com/changlongjianggit/a2j-transformer
공식 코드https://github.com/cpantazop/handrepo
공식 코드Monocular 3D Hand Pose Estimation with Implicit Camera Alignment
https://github.com/cwc1260/handdagt
공식 코드HandDAGT: A Denoising Adaptive Graph Transformer for 3D Hand Pose Estimation
https://github.com/cwc1260/handdiff
공식 코드HandDiff: 3D Hand Pose Estimation with Diffusion on Image-Point Cloud
https://github.com/cwc1260/handfold
공식 코드https://github.com/dahiyaaneesh/peclr
공식 코드https://github.com/damo-cv/motionrgbd
공식 코드Decoupling and Recoupling Spatiotemporal Representation for RGB-D-based Motion Recognition
https://github.com/deepwise888/ochid-fi
공식 코드OCHID-Fi: Occlusion-Robust Hand Pose Estimation in 3D via RF-Vision
https://github.com/delaprada/handsynthesis
공식 코드Analyzing the Synthetic-to-Real Domain Gap in 3D Hand Pose Estimation
https://github.com/deyingk/agmn
공식 코드https://github.com/doughtmw/hmd-ego-pose
공식 코드https://github.com/dubiouscactus/meta-learning-hope
공식 코드https://github.com/dxli94/WLASL
공식 코드https://github.com/edybk/hand-pose-estimation-for-surgical-training
공식 코드Using Hand Pose Estimation To Automate Open Surgery Training Feedback
https://github.com/eth-ait/tempclr
공식 코드TempCLR: Reconstructing Hands via Time-Coherent Contrastive Learning
https://github.com/exoskelebox/force-myography-hand-gesture-recognition-benchmark-data
공식 코드Force myography benchmark data for hand gesture recognition and transfer learning
https://github.com/facebookresearch/body2hands
공식 코드Body2Hands: Learning to Infer 3D Hands from Conversational Gesture Body Dynamics
https://github.com/facebookresearch/emg2pose
공식 코드emg2pose: A Large and Diverse Benchmark for Surface Electromyographic Hand Pose Estimation
https://github.com/facebookresearch/frankmocap
공식 코드FrankMocap: Fast Monocular 3D Hand and Body Motion Capture by Regression and Integration
https://github.com/facebookresearch/multi_view_active_learning
공식 코드https://github.com/fandulu/DD-Net
공식 코드Make Skeleton-based Action Recognition Model Smaller, Faster and Better
https://github.com/fanglinpu/JGR-P2O
공식 코드https://github.com/feichtenhofer/twostreamfusion
공식 코드Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/fhagelskjaer/pin-segmentation
공식 코드In-Hand Pose Estimation and Pin Inspection for Insertion of Through-Hole Components
https://github.com/florasion/d-vqvae
공식 코드Decomposed Vector-Quantized Variational Autoencoder for Human Grasp Generation
https://github.com/fylwen/htt
공식 코드https://github.com/guglielmocamporese/hands-segmentation-pytorch
공식 코드https://github.com/hassanali-90/snapture
공식 코드Snapture -- A Novel Neural Architecture for Combined Static and Dynamic Hand Gesture Recognition
https://github.com/hassony2/homan
공식 코드Towards unconstrained joint hand-object reconstruction from RGB videos
https://github.com/hongsukchoi/Pose2Mesh_RELEASE
공식 코드Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose
https://github.com/hsp-iit/fast-ycb
공식 코드ROFT: Real-Time Optical Flow-Aided 6D Object Pose and Velocity Tracking
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.