Yangqing Hou — accepted AI-conference papers

2026

Improving Sparse IMU-based Motion Capture with Motion Label Smoothing

Zhaorui Meng, Lu Yin, Yangqing Hou, Anjun Chen, Shihui Guo, Yipeng Qin

AAAI 2026technical

Sparse Inertial Measurement Units (IMUs) based human motion capture has gained significant momentum, driven by the adaptation of fundamental AI tools such as recurrent neural networks (RNNs) and transformers that are tailored for temporal and spatial modeling. Despite these achievements, current res

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2025

ToF-IP: Time-of-Flight Enhanced Sparse Inertial Poser for Real-time Human Motion Capture

Yuan Yao, Shifan Jiang, Yangqing Hou, Chengxu Zuo, Xinrui Chen, Shihui Guo +1

NeurIPS 2025poster

Sparse inertial measurement units (IMUs) provide a portable, low-cost solution for human motion tracking but struggle with error accumulation from drift and sensor noise when estimating joint position through time-based linear acceleration integration (i.e., indirect measurement). To address this,…

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