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

## TL;DR

SkeleMotion introduces a novel skeleton image representation that explicitly encodes motion dynamics at multiple temporal scales, improving 3D action recognition accuracy on large datasets.

## Contribution

The paper proposes SkeleMotion, a new skeleton image encoding that captures long-range joint interactions and filters noise, enhancing CNN-based 3D action recognition.

## Key findings

- Outperforms state-of-the-art on NTU RGB+D 120 dataset
- Effectively captures long-range joint interactions
- Improves robustness to noisy motion data

## Abstract

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.