# A New Representation of Skeleton Sequences for 3D Action Recognition

arXiv: https://arxiv.org/abs/1703.03492 · HTML: https://mlsanity.com/paper/1703.03492/

## Key facts

- Cited by 126 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2017.

## Built on by (most cited first, 30 of 126)

- [Revisiting Skeleton-based Action Recognition](https://mlsanity.com/paper/2104.13586/) (2021)
- [3D Human Action Representation Learning via Cross-View Consistency Pursuit](https://mlsanity.com/paper/2104.14466/) (2021)
- [Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition](https://mlsanity.com/paper/2112.03590/) (2021)
- [Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2206.13028/) (2022)
- [Skeleton Cloud Colorization for Unsupervised 3D Action Representation Learning](https://mlsanity.com/paper/2108.01959/) (2021)
- [MVSTER: Epipolar Transformer for Efficient Multi-View Stereo](https://mlsanity.com/paper/2204.07346/) (2022)
- [Masked Motion Predictors are Strong 3D Action Representation Learners](https://mlsanity.com/paper/2308.07092/) (2023)
- [CMD: Self-supervised 3D Action Representation Learning with Cross-modal Mutual Distillation](https://mlsanity.com/paper/2208.12448/) (2022)
- [LLMs are Good Action Recognizers](https://mlsanity.com/paper/2404.00532/) (2024)
- [Hypergraph Transformer for Skeleton-based Action Recognition](https://mlsanity.com/paper/2211.09590/) (2022)
- [Hierarchical Temporal Transformer for 3D Hand Pose Estimation and Action Recognition from Egocentric RGB Videos](https://mlsanity.com/paper/2209.09484/) (2022)
- [Contrastive Positive Mining for Unsupervised 3D Action Representation Learning](https://mlsanity.com/paper/2208.03497/) (2022)
- [IGFormer: Interaction Graph Transformer for Skeleton-based Human Interaction Recognition](https://mlsanity.com/paper/2207.12100/) (2022)
- [Hierarchical Consistent Contrastive Learning for Skeleton-Based Action Recognition with Growing Augmentations](https://mlsanity.com/paper/2211.13466/) (2022)
- [Hierarchically Self-Supervised Transformer for Human Skeleton Representation Learning](https://mlsanity.com/paper/2207.09644/) (2022)
- [Topology-aware Convolutional Neural Network for Efficient Skeleton-based Action Recognition](https://mlsanity.com/paper/2112.04178/) (2021)
- [GeomNet: A Neural Network Based on Riemannian Geometries of SPD Matrix Space and Cholesky Space for 3D Skeleton-Based Interaction Recognition](https://mlsanity.com/paper/2111.13089/) (2021)
- [Motion Question Answering via Modular Motion Programs](https://mlsanity.com/paper/2305.08953/) (2023)
- [BASAR:Black-box Attack on Skeletal Action Recognition](https://mlsanity.com/paper/2103.05266/) (2021)
- [DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition](https://mlsanity.com/paper/2210.05895/) (2022)
- [Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action Recognition](https://mlsanity.com/paper/2108.04536/) (2021)
- [Self-supervised Action Representation Learning from Partial Spatio-Temporal Skeleton Sequences](https://mlsanity.com/paper/2302.09018/) (2023)
- [Understanding the Robustness of Skeleton-based Action Recognition under Adversarial Attack](https://mlsanity.com/paper/2103.05347/) (2021)
- [Joint-bone Fusion Graph Convolutional Network for Semi-supervised Skeleton Action Recognition](https://mlsanity.com/paper/2202.04075/) (2022)
- [Zero-shot Skeleton-based Action Recognition via Mutual Information Estimation and Maximization](https://mlsanity.com/paper/2308.03950/) (2023)
- [Generative Adversarial Graph Convolutional Networks for Human Action Synthesis](https://mlsanity.com/paper/2110.11191/) (2021)
- [Temporal-Viewpoint Transportation Plan for Skeletal Few-shot Action Recognition](https://mlsanity.com/paper/2210.16820/) (2022)
- [Part Aware Contrastive Learning for Self-Supervised Action Recognition](https://mlsanity.com/paper/2305.00666/) (2023)
- [Prompted Contrast with Masked Motion Modeling: Towards Versatile 3D Action Representation Learning](https://mlsanity.com/paper/2308.03975/) (2023)
- [Recognizing Actions in Videos from Unseen Viewpoints](https://mlsanity.com/paper/2103.16516/) (2021)

## Abstract (arXiv)

This paper presents a new method for 3D action recognition with skeleton sequences (i.e., 3D trajectories of human skeleton joints). The proposed method first transforms each skeleton sequence into three clips each consisting of several frames for spatial temporal feature learning using deep neural networks. Each clip is generated from one channel of the cylindrical coordinates of the skeleton sequence. Each frame of the generated clips represents the temporal information of the entire skeleton sequence, and incorporates one particular spatial relationship between the joints. The entire clips include multiple frames with different spatial relationships, which provide useful spatial structural information of the human skeleton. We propose to use deep convolutional neural networks to learn long-term temporal information of the skeleton sequence from the frames of the generated clips, and then use a Multi-Task Learning Network (MTLN) to jointly process all frames of the generated clips in parallel to incorporate spatial structural information for action recognition. Experimental results clearly show the effectiveness of the proposed new representation and feature learning method for 3D action recognition.