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Skeleton-based Action Recognition
| Date | Stars |
|---|---|
| 2026-07-24 | 685 |
| 2026-07-25 | 685 |
| 2026-07-28 | 685 |
| 2026-07-30 | 685 |
| 2026-08-06 | 685 |
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growth rate 0.00%/day
# Awesome-Skeleton-based-Action-Recognition <!-- omit in toc --> If you have any problems, suggestions or improvements, please submit the issue or PR. ## TODO <!-- omit in toc --> - [ ] Paper list - [x] supervised methods - [ ] semi-supervised methods - [ ] unsupervised methods - [ ] adversarial methods - [ ] Leaderboard for supervised methods - [x] NTU RGB+D - [ ] NTU RGB+D 120 - [ ] Leaderboard for unsupervised and semi-supervised methods ## Contents <!-- omit in toc --> - [Misc](#misc) - [Datasets](#datasets) - [Semi-supervised and Unsupervised Skeleton Rrepresentation](#semi-supervised-and-unsupervised-skeleton-rrepresentation) - [arXiv](#arxiv) - [papers](#papers) - [Supervised Skeleton-based Action Recognition](#supervised-skeleton-based-action-recognition) - [arXiv papers](#arxiv-papers) - [Survey](#survey) - [2020](#2020) - [2019](#2019) - [2018](#2018) - [2017](#2017) - [before 2017](#before-2017) - [LeaderBoard](#leaderboard) - [NTU-RGB+D](#ntu-rgbd) - [NTU-RGB+D 120](#ntu-rgbd-120) ## Misc - Microsoft Kinect sensor and its effect (**IEEE Multimedia 2012**) [[paper](https://ieeexplore.ieee.org/document/6190806)] - Other GITHUB Repos for Skeleton-based Action Recognition Papers - [<https://github.com/XiaoCode-er/Skeleton-Based-Action-Recognition-Papers>](https://github.com/XiaoCode-er/Skeleton-Based-Action-Recognition-Papers) - [<https://github.com/cagbal/Skeleton-Based-Action-Recognition-Papers-and-Notes>](https://github.com/cagbal/Skeleton-Based-Action-Recognition-Papers-and-Notes) - [Quo Vadis, Skeleton Action Recognition?](https://skeleton.iiit.ac.in/) : A web portal as part on human action understanding from skeleton data. The portal contains - (1) an interactive dashboard showing detailed performance plots of top performing models for NTU-120 dataset. - (2) code and pre-trained models for top-performers, including novel ensemble which achieves state-of-the-art performance on NTU-120 - (3) new skeleton action datasets (skeletics-152, skeleton-mimetics) and pre-trained models. ## Datasets - *(New! 2021)* **PoseC3D 2D Skeleton Dataset (FineGYM, NTURGB-D, Kinetics, Volleyball)** [[arxiv](https://arxiv.org/pdf/2104.13586.pdf), [Github](https://github.com/kennymckormick/pyskl)] - *(New! 2021)* **NTU60-X Dataset** [[arxiv](https://arxiv.org/pdf/2101.11529.pdf), [Github](https://github.com/skelemoa/ntu-x)] - *(New! 2019)* **NTU RGB+D 120 Dataset** [[Homepage](http://rose1.ntu.edu.sg/datasets/actionrecognition.asp),[Github](https://github.com/shahroudy/NTURGB-D)] - NTU RGB+D Dataset [[Homepage](http://rose1.ntu.edu.sg/datasets/actionrecognition.asp),[Github](https://github.com/shahroudy/NTURGB-D)] - (2018) VARYING-VIEW RGB-D ACTION DATASET [[arxiv](https://arxiv.org/pdf/1904.10681.pdf), [Github](https://github.com/HRI-UESTC/CFM-HRI-RGB-D-action-database)] - (2017) SYSU 3D Human-Object Interaction Dataset (**SYSU**) - (2015) UWA3D Multiview Activity II Dataset (**UWA3D**) [[download](http://staffhome.ecm.uwa.edu.au/~00053650/databases.html)] - (2014) Northwestern-UCLA Dataset (**N-UCLA**) [[donwload](https://users.eecs.northwestern.edu/~jwa368/my_data.html)] <!-- - SBU Kinect Interaction Dataset (**SBU**) --> This section only shows some popular or new datasets, other available datasets for 3D action recognition and their statistics can be found in the following Table from the journal paper of **NTU RGB+D 120 Dataset** ([TPAMI](https://arxiv.org/pdf/1905.04757.pdf)).  ## Semi-supervised and Unsupervised Skeleton Rrepresentation ### arXiv - **[Kinetic-GAN]** Generative Adversarial Graph Convolutional Networks for Human Action Synthesis (**WACV 2022**)[[arxiv](https://arxiv.org/abs/2110.11191)] [[Github](https://github.com/DegardinBruno/Kinetic-GAN)] - Augmented skeleton based contrastive action learning with momentum lstm for unsupervised action recognition [[arxiv](https://arxiv.org/abs/2008.00188)] [[Github](https://github.com/LZU-SIAT/A
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Anirudh Thatipelli · PhD, University of Central Florida · United States
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Haodong Duan · ByteDance · Singapore
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Bruno Degardin
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:4fcead3355623242, topic:computer-vision