# Application of Graph Convolutions in a Lightweight Model for Skeletal Human Motion Forecasting

arXiv: https://arxiv.org/abs/2110.04810 · HTML: https://mlsanity.com/paper/2110.04810/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 11 references that are in the map.
- First posted on arXiv 10 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [On human motion prediction using recurrent neural networks](https://mlsanity.com/paper/1705.02445/) (2017)
- [Learning Trajectory Dependencies for Human Motion Prediction](https://mlsanity.com/paper/1908.05436/) (2019)
- [Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction](https://mlsanity.com/paper/2003.08802/) (2020)
- [Modeling Human Motion with Quaternion-based Neural Networks](https://mlsanity.com/paper/1901.07677/) (2019)
- [Temporal Extension Module for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2003.08951/) (2020)
- [Recurrent Network Models for Human Dynamics](https://mlsanity.com/paper/1508.00271/) (2015)

## Built on by (most cited first, 2 of 2)

- [SPOTR: Spatio-temporal Pose Transformers for Human Motion Prediction](https://mlsanity.com/paper/2303.06277/) (2023)
- [KP-RNN: A Deep Learning Pipeline for Human Motion Prediction and Synthesis of Performance Art](https://mlsanity.com/paper/2210.04366/) (2022)

## Abstract (arXiv)

Prediction of movements is essential for successful cooperation with intelligent systems. We propose a model that integrates organized spatial information as given through the moving body's skeletal structure. This inherent structure is exploited in our model through application of Graph Convolutions and we demonstrate how this allows leveraging the structured spatial information into competitive predictions that are based on a lightweight model that requires a comparatively small number of parameters.