Application of Graph Convolutions in a Lightweight Model for Skeletal Human Motion Forecasting
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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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 11 references that are in the map. First posted on arXiv 10 Oct 2021.
Builds on
9 of 11 references.
References · 9
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
- 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 820
- 2017On human motion prediction using recurrent neural networkscited by 336
- 2019Learning Trajectory Dependencies for Human Motion Predictioncited by 245
- 2020Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Predictioncited by 132
- 2019Modeling Human Motion with Quaternion-based Neural Networkscited by 45
- 2020Temporal Extension Module for Skeleton-Based Action Recognitioncited by 7
- 2015Recurrent Network Models for Human Dynamicscited by 4
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Citing papers per year: 2022: 1; 2023: 1.
2 papers.