# Investigation of Different Skeleton Features for CNN-based 3D Action Recognition

arXiv: https://arxiv.org/abs/1705.00835 · HTML: https://mlsanity.com/paper/1705.00835/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2017.

## Built on by (most cited first, 12 of 12)

- [SkateFormer: Skeletal-Temporal Transformer for Human Action Recognition](https://mlsanity.com/paper/2403.09508/) (2024)
- [Learn to Predict How Humans Manipulate Large-sized Objects from Interactive Motions](https://mlsanity.com/paper/2206.12612/) (2022)
- [IIP-Transformer: Intra-Inter-Part Transformer for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2110.13385/) (2021)
- [Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition](https://mlsanity.com/paper/2209.10073/) (2022)
- [The Imaginative Generative Adversarial Network: Automatic Data Augmentation for Dynamic Skeleton-Based Hand Gesture and Human Action Recognition](https://mlsanity.com/paper/2105.13061/) (2021)
- [Skeleton Sequence and RGB Frame Based Multi-Modality Feature Fusion Network for Action Recognition](https://mlsanity.com/paper/2202.11374/) (2022)
- [Human Pose-based Estimation, Tracking and Action Recognition with Deep Learning: A Survey](https://mlsanity.com/paper/2310.13039/) (2023)
- [3D Skeleton-Based Action Recognition: A Review](https://mlsanity.com/paper/2506.00915/) (2025)
- [MK-SGN: A Spiking Graph Convolutional Network with Multimodal Fusion and Knowledge Distillation for Skeleton-based Action Recognition](https://mlsanity.com/paper/2404.10210/) (2024)
- [3D Plant Root Skeleton Detection and Extraction](https://mlsanity.com/paper/2508.08094/) (2025)
- [FT-HID: A Large Scale RGB-D Dataset for First and Third Person Human Interaction Analysis](https://mlsanity.com/paper/2209.10155/) (2022)
- [SkelVIT: Consensus of Vision Transformers for a Lightweight Skeleton-Based Action Recognition System](https://mlsanity.com/paper/2311.08094/) (2023)

## Abstract (arXiv)

Deep learning techniques are being used in skeleton based action recognition tasks and outstanding performance has been reported. Compared with RNN based methods which tend to overemphasize temporal information, CNN-based approaches can jointly capture spatio-temporal information from texture color images encoded from skeleton sequences. There are several skeleton-based features that have proven effective in RNN-based and handcrafted-feature-based methods. However, it remains unknown whether they are suitable for CNN-based approaches. This paper proposes to encode five spatial skeleton features into images with different encoding methods. In addition, the performance implication of different joints used for feature extraction is studied. The proposed method achieved state-of-the-art performance on NTU RGB+D dataset for 3D human action analysis. An accuracy of 75.32% was achieved in Large Scale 3D Human Activity Analysis Challenge in Depth Videos.