Kinematics-Induced Multimodal 3D Human Pose Estimation with Subject-Level Privacy

The paper introduces a unified framework for multimodal 3D human pose estimation that fuses RGB, LiDAR, and mmWave radar data while incorporating kinematics-based sensor fusion. It presents a black-box subject membership inference attack and a pointwise maximal leakage analysis to assess privacy risks, and proposes a user-level differential privacy method called Action Temporal Stratification to mitigate these risks. The framework is evaluated on the MM-Fi dataset under three experimental protocols, with source code to be released upon acceptance.

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• computer-vision
• multimodal
• safety