Shuting Zhao — accepted AI-conference papers

2026

KineST: A Kinematics-guided Spatiotemporal State Space Model for Human Motion Tracking from Sparse Signals

AAAI 2026technical

Full-body motion tracking plays an essential role in AR/VR applications, bridging physical and virtual interactions. However, it is challenging to reconstruct realistic and diverse full-body poses based on sparse signals obtained by head-mounted displays, which are the main devices in AR/VR scenario

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2025

Remote: Real-Time Ego-Motion Tracking for Various Endoscopes via Multimodal Visual Feature Learning

Liangjing Shao, Benshuang Chen, Shuting Zhao, Xinrong Chen

ICRA 2025

Real-time ego-motion tracking for endoscope is a significant task for efficient navigation and robotic automation of endoscopy. In this paper, a novel framework is proposed to perform real-time ego-motion tracking for endoscope. Firstly, a multi-modal visual feature learning network is proposed to p

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