# SkeleMotion: A New Representation of Skeleton Joint Sequences Based on Motion Information for 3D Action Recognition

arXiv: https://arxiv.org/abs/1907.13025 · HTML: https://mlsanity.com/paper/1907.13025/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2019.

## Built on by (most cited first, 30 of 43)

- [Revisiting Skeleton-based Action Recognition](https://mlsanity.com/paper/2104.13586/) (2021)
- [PSTNet: Point Spatio-Temporal Convolution on Point Cloud Sequences](https://mlsanity.com/paper/2205.13713/) (2022)
- [STAR-Transformer: A Spatio-temporal Cross Attention Transformer for Human Action Recognition](https://mlsanity.com/paper/2210.07503/) (2022)
- [Motion Question Answering via Modular Motion Programs](https://mlsanity.com/paper/2305.08953/) (2023)
- [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)
- [No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces](https://mlsanity.com/paper/2203.11113/) (2022)
- [On the Utility of 3D Hand Poses for Action Recognition](https://mlsanity.com/paper/2403.09805/) (2024)
- [Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition](https://mlsanity.com/paper/2110.13008/) (2021)
- [An Information Compensation Framework for Zero-Shot Skeleton-based Action Recognition](https://mlsanity.com/paper/2406.00639/) (2024)
- [Action Segmentation Using 2D Skeleton Heatmaps and Multi-Modality Fusion](https://mlsanity.com/paper/2309.06462/) (2023)
- [Expressive Keypoints for Skeleton-based Action Recognition via Skeleton Transformation](https://mlsanity.com/paper/2406.18011/) (2024)
- [3D Skeleton-Based Action Recognition: A Review](https://mlsanity.com/paper/2506.00915/) (2025)
- [Are Spatial-Temporal Graph Convolution Networks for Human Action Recognition Over-Parameterized?](https://mlsanity.com/paper/2505.10679/) (2025)
- [IMoRe: Implicit Program-Guided Reasoning for Human Motion Q&A](https://mlsanity.com/paper/2508.01984/) (2025)
- [Spatial-temporal Transformer-guided Diffusion based Data Augmentation for Efficient Skeleton-based Action Recognition](https://mlsanity.com/paper/2302.13434/) (2023)
- [Deep video representation learning: a survey](https://mlsanity.com/paper/2405.06574/) (2024)
- [Zero-Shot Skeleton-Based Action Recognition With Prototype-Guided Feature Alignment](https://mlsanity.com/paper/2507.00566/) (2025)
- [Strengthening Skeletal Action Recognizers via Leveraging Temporal Patterns](https://mlsanity.com/paper/2205.14405/) (2022)
- [AFE-CNN: 3D Skeleton-based Action Recognition with Action Feature Enhancement](https://mlsanity.com/paper/2208.03444/) (2022)
- [Transformers in Action Recognition: A Review on Temporal Modeling](https://mlsanity.com/paper/2302.01921/) (2022)
- [Learning by Aligning 2D Skeleton Sequences and Multi-Modality Fusion](https://mlsanity.com/paper/2305.19480/) (2023)
- [EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation](https://mlsanity.com/paper/2603.18739/) (2026)
- [Towards Automated and Marker-less Parkinson Disease Assessment: Predicting UPDRS Scores using Sit-stand videos](https://mlsanity.com/paper/2104.04650/) (2021)
- [Action Recognition with Domain Invariant Features of Skeleton Image](https://mlsanity.com/paper/2111.11250/) (2021)
- [Measuring Student Behavioral Engagement using Histogram of Actions](https://mlsanity.com/paper/2307.09420/) (2023)
- [Frequency-Guided Multi-Level Human Action Anomaly Detection with Normalizing Flows](https://mlsanity.com/paper/2404.17381/) (2024)
- [Signal-SGN: A Spiking Graph Convolutional Network for Skeletal Action Recognition via Learning Temporal-Frequency Dynamics](https://mlsanity.com/paper/2408.01701/) (2024)
- [EPAM-Net: An Efficient Pose-driven Attention-guided Multimodal Network for Video Action Recognition](https://mlsanity.com/paper/2408.05421/) (2024)
- [Content-Distortion High-Order Interaction for Blind Image Quality Assessment](https://mlsanity.com/paper/2504.05076/) (2025)

## Abstract (arXiv)

Due to the availability of large-scale skeleton datasets, 3D human action recognition has recently called the attention of computer vision community. Many works have focused on encoding skeleton data as skeleton image representations based on spatial structure of the skeleton joints, in which the temporal dynamics of the sequence is encoded as variations in columns and the spatial structure of each frame is represented as rows of a matrix. To further improve such representations, we introduce a novel skeleton image representation to be used as input of Convolutional Neural Networks (CNNs), named SkeleMotion. The proposed approach encodes the temporal dynamics by explicitly computing the magnitude and orientation values of the skeleton joints. Different temporal scales are employed to compute motion values to aggregate more temporal dynamics to the representation making it able to capture longrange joint interactions involved in actions as well as filtering noisy motion values. Experimental results demonstrate the effectiveness of the proposed representation on 3D action recognition outperforming the state-of-the-art on NTU RGB+D 120 dataset.