Summary of Context-based Interpretable Spatio-temporal Graph Convolutional Network For Human Motion Forecasting, by Edgar Medina et al.
Context-based Interpretable Spatio-Temporal Graph Convolutional Network for Human Motion Forecasting
by Edgar Medina, Leyong Loh, Namrata Gurung, Kyung Hun Oh, Niels Heller
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel architecture, Context-based Interpretable Spatio-Temporal Graph Convolutional Network (CIST-GCN), is proposed for 3D human pose forecasting in autonomous driving and safety applications. The CIST-GCN model leverages GCNs to extract meaningful information from pose sequences, aggregate displacements and accelerations, and predict output displacements. Compared to previous methods, the CIST-GCN outperforms on Human 3.6M, AMASS, 3DPW, and ExPI datasets in terms of human motion prediction and robustness. The paper also explores interpretability enhancements for motion prediction, providing preliminary evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Human motion prediction is crucial for autonomous driving and safety applications. Researchers have developed a new model called CIST-GCN to predict human poses in 3D space. This model uses something called GCNs to understand patterns in human movement. It takes into account how people move their bodies, including things like distance traveled and speed. The results show that this model is better than other models at predicting human motion and staying consistent even when the data is different. |
Keywords
» Artificial intelligence » Convolutional network » Gcn
