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Hand

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/hsp-iit/roft

공식 코드

ROFT: Real-Time Optical Flow-Aided 6D Object Pose and Velocity Tracking

https://github.com/hukenovs/hagrid

공식 코드

HaGRID - HAnd Gesture Recognition Image Dataset

https://github.com/humansensinglab/Hamba

공식 코드

Hamba: Single-view 3D Hand Reconstruction with Graph-guided Bi-Scanning Mamba

https://github.com/hxwork/handbooster_pytorch

공식 코드

HandBooster: Boosting 3D Hand-Mesh Reconstruction by Conditional Synthesis and Sampling of Hand-Object Interactions

https://github.com/hzwang96/pegg-net

공식 코드

PEGG-Net: Pixel-Wise Efficient Grasp Generation in Complex Scenes

https://github.com/intelligolabs/oo-dmvmt

공식 코드

OO-dMVMT: A Deep Multi-view Multi-task Classification Framework for Real-time 3D Hand Gesture Classification and Segmentation

https://github.com/iscas3dv/handpose-virtualview

공식 코드

Efficient Virtual View Selection for 3D Hand Pose Estimation

https://github.com/istiakshihab/automated-retail-checkout-aicity22

공식 코드

VISTA: Vision Transformer enhanced by U-Net and Image Colorfulness Frame Filtration for Automatic Retail Checkout

https://github.com/ivalab/kgn

공식 코드

Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D input

https://github.com/jin-s13/COCO-WholeBody

공식 코드

Whole-Body Human Pose Estimation in the Wild

https://github.com/jkpark0825/eanet

공식 코드

Extract-and-Adaptation Network for 3D Interacting Hand Mesh Recovery

https://github.com/junggy/housecat6d

공식 코드

HouseCat6D -- A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios

https://github.com/kenshohara/3D-ResNets

공식 코드

Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition

https://github.com/korrawe/grasping_field

공식 코드

Grasping Field: Learning Implicit Representations for Human Grasps

https://github.com/korrawe/grasping_field_demo

공식 코드

Grasping Field: Learning Implicit Representations for Human Grasps

https://github.com/leolyliu/teg-track

공식 코드

Enhancing Generalizable 6D Pose Tracking of an In-Hand Object with Tactile Sensing

https://github.com/leylakhaleghi/muvihand

공식 코드

Multi-View Video-Based 3D Hand Pose Estimation

https://github.com/lijenchang/Mask2Hand

공식 코드

Mask2Hand: Learning to Predict the 3D Hand Pose and Shape from Shadow

https://github.com/lixiny/artiboost

공식 코드

ArtiBoost: Boosting Articulated 3D Hand-Object Pose Estimation via Online Exploration and Synthesis

https://github.com/lixiny/oakink

공식 코드

OakInk: A Large-scale Knowledge Repository for Understanding Hand-Object Interaction

https://github.com/lmb-freiburg/hand3d

공식 코드

Learning to Estimate 3D Hand Pose from Single RGB Images

https://github.com/mallikagarg/convmixformer

공식 코드

ConvMixFormer- A Resource-efficient Convolution Mixer for Transformer-based Dynamic Hand Gesture Recognition

https://github.com/mallikagarg/gestformer

공식 코드

GestFormer: Multiscale Wavelet Pooling Transformer Network for Dynamic Hand Gesture Recognition

https://github.com/mallikagarg/mvtn

공식 코드

MVTN: A Multiscale Video Transformer Network for Hand Gesture Recognition

https://github.com/masabdi/LSPS

공식 코드

3D Hand Pose Estimation using Simulation and Partial-Supervision with a Shared Latent Space

https://github.com/meghshukla/activelearningforhumanpose

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VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimation

https://github.com/melonwan/denseReg

공식 코드

Dense 3D Regression for Hand Pose Estimation

https://github.com/menghao666/hdr

공식 코드

3D Interacting Hand Pose Estimation by Hand De-occlusion and Removal

https://github.com/microsoft/MeshTransformer

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End-to-End Human Pose and Mesh Reconstruction with Transformers

https://github.com/microsoft/meshgraphormer

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Mesh Graphormer

https://github.com/mikhail-vlasenko/grlib

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GRLib: An Open-Source Hand Gesture Detection and Recognition Python Library

https://github.com/mims-harvard/tfc-pretraining

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Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency

https://github.com/mks0601/Hand4Whole_RELEASE

공식 코드

Accurate 3D Hand Pose Estimation for Whole-Body 3D Human Mesh Estimation

https://github.com/mks0601/I2L-MeshNet_RELEASE

공식 코드

I2L-MeshNet: Image-to-Lixel Prediction Network for Accurate 3D Human Pose and Mesh Estimation from a Single RGB Image

https://github.com/mks0601/V2V-PoseNet_RELEASE

공식 코드

V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map

https://github.com/moberweger/deep-prior

공식 코드

Hands Deep in Deep Learning for Hand Pose Estimation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.