Doppler Prompting for Stable mmWave-based Human Pose Estimation
For researchers in privacy-preserving human pose estimation, PULSE addresses the problem of jittery trajectories caused by misinterpreted non-human Doppler signatures.
PULSE improves mmWave-based human pose estimation by converting Doppler signatures into confidence-aware motion prompts that are screened before influencing prediction, achieving consistent gains in pose accuracy and temporal stability across three datasets.
Millimeter-wave (mmWave) enables privacy-preserving, illumination-robust human pose estimation (HPE), with each mmWave frame represented as a range-angle-Doppler tensor, providing spatial magnitude for localization and Doppler signatures for motion-related cues. However, existing mmWave-based HPE methods either underutilize or naïvely fuse Doppler signatures with spatial magnitude, disregarding their distinct physical semantics. As a result, non-human Doppler signatures can be misinterpreted as human motion cues, leading to jittery trajectories. We propose PULSE, which converts Doppler signatures into confidence-aware motion prompts and injects them into spatial magnitude reasoning through constrained interactions. By screening Doppler prompts before they influence prediction, PULSE first suppresses spurious spectral motion cues and then uses the screened prompts to stabilize prediction. Across three datasets spanning single- and multi-person settings, PULSE consistently improves pose accuracy and temporal stability, indicating that controlled Doppler prompting is a practical direction for stable mmWave HPE.