Awesome-Skeleton-Based-Models

July 6, 2022 · View on GitHub

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Awesome-Skeleton-Based-Models

We collect existing skeleton-based models (369+ Papers & Codes) published in prominent conferences (CVPR, ICCV, ECCV, AAAI, IJCAI, ACMMM, ICLR, ICML, NeurIPS, etc) and journals (TPAMI, IJCV, TIP, TMM, TNNLS, PMLR, etc).

TODO

  • Paper List
    • Skeleton-Based Action Recognition
    • 3D Pose Estimation
    • Skeleton-Based Person Re-Identification (New!)
    • Skeleton-Based Gesture Recognition
    • Skeleton-Based Gait Recognition
    • Others (Skeleton-Based Motion Prediction, Interaction Recognition, etc)

Contents

Skeleton-Based Action Recognition (2013-2022)

70 Datasets

Overview of 70 available datasets for action recognition and their statistics, provided by the paper of (TPAMI 2022) [arxiv] (Human Action Recognition from Various Data Modalities: A Review).

S: Skeleton, D: Depth, IR: Infrared, PC: Point Cloud, ES: Event Stream, Au: Audio, Ac: Acceleration, Gyr: Gyroscope, EMG: Electromyography. Bold shows the most-frequently used datasets in the literature.
# IdDatasetYearModality# Class# Subject# Sample# View
1KTH2004RGB6252,3911
2Weizmann2005RGB109901
3IXMAS2006RGB11103305
4HDM052007RGB,S13052,3371
5Hollywood2008RGB8—430—
6Hollywood22009RGB12—3,669—
7MSR-Action3D2010S,D20105671
8Olympic2010RGB16—783—
9CAD-602011RGB,S,D12460—
10HMDB512011RGB51—6,766—
11RGB-HuDaAct2011RGB,D13301,1891
12ACT4^{2}2012RGB,D14246,8444
13DHA2012RGB,D17213571
14MSRDailyActivity3D2012RGB,S,D16103201
15UCF1012012RGB101—13,320—
16UTKinect2012RGB,S,D10102001
17Berkeley MHAD2013RGB,S,D,Au,Ac12126604
18CAD-1202013RGB,S,D104120—
19IAS-lab2013RGB,S,D,PC15125401
20J-HMDB2013RGB,S21—31,838—
21MSRAction-Pair2013RGB,S,D12103601
22UCFKinect2013S16161,2801
23Multi-View TJU2014RGB,S,D20227,0402
24Northwestern-UCLA2014RGB,S,D10101,4753
25Sports-1M2014RGB487—$1,113,158$—
26UPCV2014S10204001
27UWA3D Multiview2014RGB,S,D3010~9004
28ActivityNet2015RGB203—27,801—
29SYSU 3D HOI2015RGB,S,D12404801
30THUMOS Challenge 152015RGB101—24,017—
31TJU2015RGB,S,D15201,2001
32UTD-MHAD2015RGB,S,D,Ac,Gyr2788611
33UWA3D Multiview II2015RGB,S,D30101,0754
34M^{2}I2015RGB,S,D2222~18002
35Charades2016RGB1572679,848—
36InfAR2016IR12406002
37NTU RGB+D2016RGB,S,D,IR604056,88080
38YouTube-8M2016RGB4,800—8,264,650—
39AVA2017RGB80—437—
40DvsGesture2017ES1729——
41FCVID2017RGB239—91,233—
42Kinetics-4002017RGB400—306,245—
43NEU-UB2017RGB,D620600—
44PKU-MMD2017RGB,S,D,IR51661,0763
45Something-Something-v12017RGB174—108,499—
46UniMiB SHAR2017Ac173011,771—
47EPIC-KITCHENS-552018RGB,Au—3239,594Egocentric
48Kinetics-6002018RGB600—495,547—
49RGB-D Varying-view2018RGB,S,D4011825,6008+1(360∘^{\circ})
50DHP192019ES,S3317—4
51Drive&Act2019RGB,S,D,IR8315—6
52Hemangomez et al.2019Radar8111,056—
53Kinetics-7002019RGB700—650,317—
54Kitchen202019Au20—800—
55MMAct2019RGB,S,Ac,Gyr,etc.372036,7644+Egocentric
56Moments in Time2019RGB339—~1,000,000—
57Wang et al.2019WiFi CSI611,394—
58NTU RGB+D 1202019RGB,S,D,IR120106114,480155
59ETRI-Activity3D2020RGB,S,D55100112,620—
60EV-Action2020RGB,S,D,EMG20707,0009
61IKEA ASM2020RGB,S,D334816,7643
62RareAct2020RGB122—905—
63BABEL2021Mocap252—13,220—
64HAA5002021RGB500—10,000—
65HOMAGE2021RGB,IR,Ac,Gyr,etc.75271,7522~5
66MultiSports2021RGB66—37,701—
67UAV-Human2021RGB,S,D,IR,etc.15511967,428—
68Ego4D2022RGB,Au,Ac,etc.—923—Egocentric
69EPIC-KICHENS-1002022RGB,Au,Ac—4589,979Egocentric
70JRDB-Act2022RGB,PC26—3,625360∘^{\circ}

Survey Papers

  • Human Action Recognition and Prediction: A Survey Recognition Algorithms (IJCV 2022) [paper]

  • Human Action Recognition from Various Data Modalities: A Review (TPAMI 2022) [arxiv]

  • A Comparative Review of Recent Kinect-based Action Recognition Algorithms (TIP 2019) [arxiv]

2022

  • Constructing Stronger and Faster Baselines for Skeleton-based Action Recognition (TPAMI 2022) [paper] [Github] (Not uploaded) [Gitee]

  • [Sym-GNN] Symbiotic Graph Neural Networks for 3D Skeleton-Based Human Action Recognition and Motion Prediction (TPAMI 2022) [paper] [Github] (Not uploaded)

  • [X-CAR] X-Invariant Contrastive Augmentation and Representation Learning for Semi-Supervised Skeleton-Based Action Recognition (TIP 2022) [paper]

  • [FGCN] Feedback Graph Convolutional Network for Skeleton-Based Action Recognition (TIP 2022) [paper]

  • [Multi-LiSAAL] Multi-Localized Sensitive Autoencoder-Attention-LSTM For Skeleton-based Action Recognition (TMM 2022) [paper]

  • [LAGA-Net] LAGA-Net: Local-and-Global Attention Network for Skeleton Based Action Recognition (TMM 2022) [paper]

  • [CIASA] Adversarial Attack on Skeleton-Based Human Action Recognition (TNNLS 2022) [paper]

  • [MTT] MTT: Multi-Scale Temporal Transformer for Skeleton-Based Action Recognition (IEEE Signal Process. Lett. 2022) [paper]

  • [Graph2Net] Graph2Net: Perceptually-Enriched Graph Learning for Skeleton-Based Action Recognition (IEEE Trans. Circuits Syst. Video Technol. 2022) [paper] [Github]

  • A Cross View Learning Approach for Skeleton-Based Action Recognition (IEEE Trans. Circuits Syst. Video Technol. 2022) [paper]

  • Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition (ACM Trans. Multim. Comput. Commun. Appl. 2022) [paper] [Github]

  • Skeleton Sequence and RGB Frame Based Multi-Modality Feature Fusion Network for Action Recognition (ACM Trans. Multim. Comput. Commun. Appl. 2022) [paper]

  • [SparseShift-GCN] SparseShift-GCN: High precision skeleton-based action recognition (Pattern Recognit. Lett. 2022) [paper]

  • [FR-AGCN] Forward-reverse adaptive graph convolutional networks for skeleton-based action recognition (Neurocomputing 2022) [paper] [Github]

  • [ED-GCN] Enhanced discriminative graph convolutional network with adaptive temporal modelling for skeleton-based action recognition (Comput. Vis. Image Underst. 2022) [paper]

2021

  • Quo Vadis, Skeleton Action Recognition ? (IJCV 2021) [paper] [Github]

  • [CTR-GCN] Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition (ICCV 2021) [paper] [Github]

  • Spatio-Temporal Difference Descriptor for Skeleton-Based Action Recognition (AAAI 2021) [paper]

  • [AdaSGN] AdaSGN: Adapting Joint Number and Model Size for Efficient Skeleton-Based Action Recognition (ICCV 2021) [paper] [Github]

  • Self-supervised 3D Skeleton Action Representation Learning with Motion Consistency and Continuity (ICCV 2021) [paper]

  • Skeleton Cloud Colorization for Unsupervised 3D Action Representation Learning (ICCV 2021) [paper]

  • 3D Human Action Representation Learning via Cross-View Consistency Pursuit (CVPR 2021) [arxiv][Github]

  • [MST-GCN] Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition (AAAI 2021) [paper] [Github]

  • Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action Recognition (ACMMM 2021) [paper] [Github] (Not uploaded)

  • Modeling the Uncertainty for Self-supervised 3D Skeleton Action Representation Learning (ACMMM 2021) [paper]

  • Skeleton-Contrastive 3D Action Representation Learning (ACMMM 2021) [paper] [Github] (Not uploaded)

  • [STST] STST: Spatial-Temporal Specialized Transformer for Skeleton-based Action Recognition (ACMMM 2021) [paper] [Github] (Not uploaded)

  • [CP-STN] Spatial Temporal Enhanced Contrastive and Pretext Learning for Skeleton-based Action Representation (PMLR 2021) [paper]

  • Tripool: Graph triplet pooling for 3D skeleton-based action recognition (Pattern Recognit. 2021) [paper]

  • Structural Knowledge Distillation for Efficient Skeleton-Based Action Recognition (TIP 2021) [paper] [Github]

  • [ShiftGCN++] Extremely Lightweight Skeleton-Based Action Recognition With ShiftGCN++ (TIP 2021) [paper] [Github]

  • [Hyper-GNN] Hypergraph Neural Network for Skeleton-Based Action Recognition (TIP 2021) [paper]

  • [GCN-HCRF] A Multi-Stream Graph Convolutional Networks-Hidden Conditional Random Field Model for Skeleton-Based Action Recognition (TMM 2021) [paper]

  • Hierarchical Soft Quantization for Skeleton-Based Human Action Recognition (TMM 2021) [paper]

  • Pose Refinement Graph Convolutional Network for Skeleton-Based Action Recognition ({IEEE} Robotics Autom. Lett. 2021) [paper] [Github]

  • [FDGCN] Skeleton-Based Action Recognition With Focusing-Diffusion Graph Convolutional Networks (IEEE Signal Process. Lett. 2021) [paper]

  • [ST-GDN] Spatial Temporal Graph Deconvolutional Network for Skeleton-Based Human Action Recognition (IEEE Trans. Circuits Syst. Video Technol. 2021) [paper]

  • Fuzzy Integral-Based {CNN} Classifier Fusion for 3D Skeleton Action Recognition (IEEE Trans. Circuits Syst. Video Technol. 2021) [paper] [Github]

  • [SEFN] Symmetrical Enhanced Fusion Network for Skeleton-Based Action Recognition (IEEE Trans. Circuits Syst. Video Technol. 2021) [paper]

  • [RA-GCN] Richly Activated Graph Convolutional Network for Robust Skeleton-Based Action Recognition (IEEE Trans. Circuits Syst. Video Technol. 2021) [paper] [Github]

  • Dual-Stream Structured Graph Convolution Network for Skeleton-Based Action Recognition (ACM Trans. Multim. Comput. Commun. Appl. 2021) [paper]

  • Action recognition using kinematics posture feature on 3D skeleton joint locations (Pattern Recognit. Lett. 2021) [paper]

  • Scene image and human skeleton-based dual-stream human action recognition (Pattern Recognit. Lett. 2021) [paper]

  • Skeleton-based action recognition using sparse spatio-temporal GCN with edge effective resistance (Neurocomputing 2021) [paper]

  • [VE-GCN] Integrating vertex and edge features with Graph Convolutional Networks for skeleton-based action recognition (Neurocomputing 2021) [paper]

  • [AMV-GCNs] Adaptive multi-view graph convolutional networks for skeleton-based action recognition (Neurocomputing 2021) [paper]

  • Rethinking the ST-GCNs for 3D skeleton-based human action recognition (Neurocomputing 2021) [paper] [Github]

  • [AAM-GCN] Attention adjacency matrix based graph convolutional networks for skeleton-based action recognition (Neurocomputing 2021) [paper]

  • [ST-TR] Skeleton-based action recognition via spatial and temporal transformer networks (Comput. Vis. Image Underst. 2021) [paper] [Github]

  • [AS-CAL] Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition (Inf. Sci. 2021) [paper] [Github]

2020

  • (Mainly from [Github], adding latest status)

  • [MV-IGNET] Learning Multi-View Interactional Skeleton Graph for Action Recognition (TPAMI 2020) [paper][Github]

  • [MS-AAGCN] Skeleton-Based Action Recognition with Multi-Stream Adaptive Graph Convolutional Networks (TIP 2020) [paper]

  • [P&C FW-AEC] PREDICT & CLUSTER: Unsupervised Skeleton Based Action Recognition (CVPR 2020) [paper]

  • [CA-GC] Context Aware Graph Convolution for Skeleton-Based Action Recognition (CVPR 2020) [paper]

  • [Shift-GCN] Skeleton-Based Action Recognition With Shift Graph Convolutional Network (CVPR 2020) [paper][Github]

  • [DMGNN] Dynamic Multiscale Graph Neural Networks for 3D Skeleton Based Human Motion Prediction (CVPR 2020) [paper]

  • [SGN] Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition (CVPR 2020) [arxiv][Github]

  • [MS-G3D] Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition (CVPR 2020) [arxiv] [Github]

  • Adversarial Self-Supervised Learning for Semi-Supervised 3D Action Recognition (ECCV 2020) [arxiv]

  • Unsupervised 3D Human Pose Representation with Viewpoint and Pose Disentanglement (ECCV 2020) [arxiv] [Github]

  • [DecoupleGCN-DropGraph] Decoupling GCN with DropGraph Module for Skeleton-Based Action Recognition (ECCV 2020) [arxiv] [Github]

  • Ms2l: Multi-task self-supervised learning for skeleton based action recognition (ACMMM 2020) [arxiv]

  • [Dynamic GCN] Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action Recognition (ACM-MM 2020)[arxiv]

  • [GCN-NAS] Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching (AAAI 2020) [arxiv] [Github]

  • [PA-ResGCN] Stronger, Faster and More Explainable: A Graph Convolutional Baseline for Skeleton-based Action Recognition (ACM-MM 2020) [arxiv] [Github]

  • [Poincare-GCN] Mix Dimension in Poincaré Geometry for 3D Skeleton-based Action Recognition (ACM-MM 2020) [arxiv]

  • [STIGCN] Spatio-Temporal Inception Graph Convolutional for Skeleton-Based Action Recognition (ACM-MM 2020) [arxiv]

  • [JOLO-GCN] JOLO-GCN: Mining Joint-Centered Light-Weight Information for Skeleton-Based Action Recognition (WACV 2021) [arxiv]

  • [ST-TR-AGCN] Spatial Temporal Transformer Network for Skeleton-based Action Recognition (Under submission at Computer Vision and Image Understanding (CVIU)) [arxiv] [Github]

  • [PCRP] Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action Recognition [arxiv] [Github]

2019

  • NTU-RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding (TPAMI 2019) [arxiv] [Homepage] [Github]
  • [VA-NN] View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition (TPAMI 2019) [arxiv] [Github]
  • Bayesian Graph Convolutional LSTM for Skeleton Based Action Recognition (ICCV 2019) [arxiv]
  • [2s-SDGCN] Spatial Residual Layer and Dense Connection Block Enhanced Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition (ICCV Workshop 2019) [paper]
  • [DGNN] Skeleton-Based Action Recognition With Directed Graph Neural Networks (CVPR 2019) [paper] [unofficial PyTorch implementation]
  • [2s-AGCN] Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition (CVPR 2019) [paper] [Github]
  • [AS-GCN] Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition (CVPR 2019) [arxiv] [Github]
  • [AGC-LSTM] An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition (CVPR 2019) [arxiv]
  • Optimized Skeleton-based Action Recognition via Sparsified Graph Regression (ACMMM 2019) [paper]
  • [Motif-STGCN] Graph CNNs with Motif and Variable Temporal Block for Skeleton-based Action Recognition (AAAI 2019) [arxiv] [Github]
  • Richly Activated Graph Convolutional Network for Action Recognition with Incomplete Skeletons (ICIP 2019) [arxiv] [Github]
  • [TSRJI] Skeleton Image Representation for 3D Action Recognition based on Tree Structure and Reference Joints (SIBGRAPI) [arxiv] [Github]
  • [SkeleMotion] SkeleMotion: A New Representation of Skeleton Joint Sequences Based on Motion Information for 3D Action Recognition (AVSS) [arxiv] [Github]

2018

  • Beyond Joints: Learning Representations from Primitive Geometries for Skeleton-based Action Recognition and Detection (TIP 2018) [paper] [Github]
  • [DPRL] Deep progressive reinforcement learning for skeleton-based action recognition (CVPR 2018) [paper]
  • [SR-TSL] Skeleton based action recognition with spatial reasoning and temporal stack learning (ECCV 2018) [arxiv]
  • [HCN] Co-occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation (IJCAI 2018) [arxiv] [Reimplementation]
  • [MAN] Memory attention networks for skeleton-based action recognition (IJCAI 2018) [arxiv] [Github]
  • [ST-GCN] Spatial temporal graph convolutional networks for skeleton-based action recognition (AAAI 2018) [arxiv] [Github]
  • Unsupervised representation learning with long-term dynamics for skeleton based action recognition (AAAI 2018) [arxiv] [Github]
  • Spatio-temporal graph convolution for skeleton based action recognition (AAAI 2018) [arxiv]
  • Part-based Graph Convolutional Network for Action Recognition (BMVC 2018) [arxiv] [Github]
  • A Fine-to-Coarse Convolutional Neural Network for 3D Human Action Recognition (BMVC 2018) [arxiv]
  • A Large-scale Varying-view RGB-D Action Dataset for Arbitrary-view Human Action Recognition (ACMMM 2018) [arxiv]
  • Unsupervised feature learning of human actions as trajectories in pose embedding manifold (WACV 2018) [arxiv]

2017

  • Jointly learning heterogeneous features for RGB-D activity recognition (TPAMI 2017) [paper]
  • [Visualization CNN] Enhanced skeleton visualization for view invariant human action recognition (Pattern Recognition 2017) [paper]
  • Global context-aware attention lstm networks for 3d action recognition (CVPR 2017) [paper]
  • [Two-stream RNN] Modeling temporal dynamics and spatial configurations of actions using two-stream recurrent neural networks (CVPR 2017) [arxiv] [Github]
  • [C-CNN + MTLN] A new representation of skeleton sequences for 3d action recognition (CVPR 2017) [arxiv]
  • [Ensemble TS-LSTM] Ensemble deep learning for skeleton-based action recognition using temporal sliding lstm networks (ICCV 2017) [paper] [Github]
  • [VA-LSTM] View adaptive recurrent neural networks for high performance human action recognition from skeleton data (ICCV 2017) [arxiv]
  • Learning action recognition model from depth and skeleton videos (ICCV 2017) [paper]
  • [STA-LSTM] An end-to-end spatio-temporal attention model for human action recognition from skeleton data (AAAI 2017) [arxiv]
  • Skeleton-based action recognition using LSTM and CNN (ICME Workshop 2017) [arxiv]
  • Skeleton-based action recognition with convolutional neural networks (ICME Workshop 2017) [arxiv]
  • PKU-MMD: A large scale benchmark for continuous multi-modal human action understanding (ACMMM Workshop 2017) [arxiv]
  • [Temporal Conv] Interpretable 3d human action analysis with temporal convolutional networks (CVPR Workshop 2017) [arxiv]

Before 2017

  • [Trust Gate ST-LSTM] Spatio-temporal lstm with trust gates for 3d human action recognition (ECCV 2016) [arxiv] [Github]
  • [Part-aware LSTM] NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis (CVPR 2016) [arxiv]
  • Rolling rotations for recognizing human actions from 3d skeletal data (CVPR 2016) [paper]
  • Co-occurrence feature learning for skeleton based action recognition using regularized deep lstm networks (AAAI 2016) [paper]
  • Skeleton based action recognition with convolutional neural network (ACPR 2015) [paper]
  • [H-RNN] Hierarchical recurrent neural network for skeleton based action recognition (CVPR 2015) [paper]
  • Jointly learning heterogeneous features for rgb-d activity recognition (CVPR 2015) [paper]
  • [LieGroup] Human action recognition by representing 3d skeletons as points in a lie group (CVPR 2014) [paper]
  • Human action recognition using a temporal hierarchy of covariance descriptors on 3d joint locations (IJCAI 2013) [paper]

arXiv papers

  • Skeleton-DML: Deep Metric Learning for Skeleton-Based One-Shot Action Recognition [arxiv][Github] (Accepted at WACV 2022)
  • Sparse Semi-Supervised Action Recognition with Active Learning [arxiv]
  • STAR: Sparse Transformer-based Action Recognition [arxiv] [Github]
  • [DenseIndRNN] Deep Independently Recurrent Neural Network (Preprint) [arxiv] [Github]
  • Skeleton-Based Action Recognition with Synchronous Local and Non-local Spatio-temporal Learning and Frequency Attention [arxiv] (Accepted at ICME 2019)
  • [DSTA-Net] Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action Recognition [arxiv] (Accepted at ACCV 2020)
  • SynSE: Syntactically Guided Generative Embeddings for Zero Shot Skeleton Action Recognition [arxiv] [Github] (Accepted at ICIP 2021)
  • [PoseC3D] Revisiting Skeleton-based Action Recognition [arxiv][Github]
  • Leveraging Third-Order Features in Skeleton-Based Action Recognition [arxiv][Github]

Skeleton-based Action Recognition under Adversarial Attack

  • Understanding the Robustness of Skeleton-based Action Recognition under Adversarial Attack (CVPR 2021) [arxiv]
  • BASAR:Black-box Attack on Skeletal Action Recognition (CVPR 2021) [arxiv]

Leaderboards on NTU-RGB+D and NTU-RGB+D 120 Datasets

NTU-RGB+D

YearMethodsCross-SubjectCross-View
2014Lie Group50.152.8
2015H-RNN59.164.0
2016Part-aware LSTM62.970.3
2016Trust Gate ST-LSTM69.277.7
2017Two-stream RNN71.379.5
2017STA-LSTM73.481.2
2017Ensemble TS-LSTM74.681.3
2017Visualization CNN76.082.6
2017C-CNN + MTLN79.684.8
2017Temporal Conv74.383.1
2017VA-LSTM79.487.6
2018Beyond Joints79.587.6
2018ST-GCN81.588.3
2018DPRL83.589.8
2019Motif-STGCN84.290.2
2018HCN86.591.1
2018SR-TSL84.892.4
2018MAN82.793.2
2019RA-GCN85.993.5
2019DenseIndRNN86.793.7
2018PB-GCN87.593.2
2019AS-GCN86.894.2
2019VA-NN (fusion)89.495.0
2019AGC-LSTM (Joint&Part)89.295.0
20192s-AGCN88.595.1
2020SGN89.094.5
2020GCN-NAS89.495.7
20192s-SDGCN89.695.7
2019DGNN89.996.1
2020MV-IGNET89.296.3
20204s Shift-GCN90.796.5
2020DecoupleGCN-DropGraph90.896.6
2020PA-ResGCN-B1990.996.0
2020MS-G3D91.596.2
2021EfficientGCN-B491.795.7
2021CTR-GCN92.496.8

NTU-RGB+D 120

YearMethodsCross-SubjectCross-Setup
2019SkeleMotion (Magnitude-Orientation)62.963.0
2019SkeleMotion + Yang et al67.766.9
2019TSRJI67.959.7
2020SGN79.281.5
2020MV-IGNET83.985.6
20204s Shift-GCN85.987.6
2020DecoupleGCN-DropGraph86.588.1
2020MS-G3D86.988.4
2020PA-ResGCN-B1987.388.3
2021EfficientGCN-B488.389.1
2021CTR-GCN88.990.6

Others

Acknowledge

If you have any problems, suggestions or improvements, please feel free to contact me (haocongrao@gmail.com). Welcome to refine current taxonomy, enrich collection of skeleton-based models, and discuss any constructive ideas.