Robust Human Trajectory Prediction via Self-Supervised Skeleton Representation Learning

• URL: http://arxiv.org/abs/2602.22791v1
• Date: Thu, 26 Feb 2026 09:25:52 GMT
• Title: Robust Human Trajectory Prediction via Self-Supervised Skeleton Representation Learning
• Authors: Taishu Arashima, Hiroshi Kera, Kazuhiko Kawamoto,
• Abstract summary: We propose a robust trajectory prediction method that incorporates a self-supervised skeleton representation model pretrained with masked autoencoding.<n> Experimental results show that our method improves robustness to missing skeletal data without sacrificing prediction accuracy, and consistently outperforms baseline models in clean-to-moderate missingness regimes.
• Score: 12.961180148172199
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human trajectory prediction plays a crucial role in applications such as autonomous navigation and video surveillance. While recent works have explored the integration of human skeleton sequences to complement trajectory information, skeleton data in real-world environments often suffer from missing joints caused by occlusions. These disturbances significantly degrade prediction accuracy, indicating the need for more robust skeleton representations. We propose a robust trajectory prediction method that incorporates a self-supervised skeleton representation model pretrained with masked autoencoding. Experimental results in occlusion-prone scenarios show that our method improves robustness to missing skeletal data without sacrificing prediction accuracy, and consistently outperforms baseline models in clean-to-moderate missingness regimes.

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